CN114228743A - Unmanned logistics vehicle control method, device and system and readable storage medium - Google Patents
Unmanned logistics vehicle control method, device and system and readable storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/005—Handover processes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/408—Radar; Laser, e.g. lidar
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/80—Spatial relation or speed relative to objects
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
- B60W2556/45—External transmission of data to or from the vehicle
- B60W2556/50—External transmission of data to or from the vehicle of positioning data, e.g. GPS [Global Positioning System] data
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention discloses a control method, a device and a system for an unmanned logistics vehicle and a readable storage medium. The method comprises the following steps: acquiring vehicle position information and driving area information, and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information; and switching the working mode of the unmanned logistics vehicle according to the correlation between the vehicle position and the driving area. The invention realizes safe and reliable switching in the automatic driving mode switching process of the vehicle.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to a control method, a device and a system for an unmanned logistics vehicle and a readable storage medium.
Background
With the development of intelligent driving technology, the safety requirement on intelligent driving of vehicles is higher and higher. The current intelligent driving comprises a single-vehicle intelligent driving mode and two modes of cloud control and driving, wherein the single-vehicle intelligent driving mode mainly depends on sensors such as vision, millimeter wave radar, laser radar and the like of a vehicle to perform environment perception, calculation decision and control execution, but technical bottlenecks of different degrees exist in multiple links of environment perception, calculation decision and control execution at present, and various failure problems are easy to occur in the application process; on the basis of a single-vehicle intelligent driving mode, the cloud control intelligent driving organically connects 'human-vehicle-road-cloud' traffic participation elements together through the internet of vehicles, and expands and assists the capacity upgrading of the single-vehicle intelligent driving mode in the aspects of environment perception, calculation decision, control execution and the like.
However, in the automatic driving process of the vehicle, because two modes exist, the two modes conflict in the mode switching process, and the cloud control intelligent driving mode and the single vehicle intelligent driving mode cannot be safely and reliably switched.
Disclosure of Invention
The invention mainly aims to provide a control method, a device and a system for an unmanned logistics vehicle and a readable storage medium. The automatic vehicle driving mode switching method aims at solving the problem that safe and reliable switching cannot be carried out in the existing automatic vehicle driving mode switching process.
In order to achieve the purpose, the invention provides a control method of an unmanned logistics vehicle, which comprises the following steps:
acquiring vehicle position information and driving area information, and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information;
and switching the working mode of the unmanned logistics vehicle according to the correlation between the vehicle position and the driving area.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations: the step of acquiring vehicle position information and driving area information and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information comprises the following steps:
acquiring a vehicle position and a preset driving area coordinate point, and determining a real-time coordinate position of the vehicle position in the preset coordinate point, wherein the vehicle position information comprises the vehicle position, and the driving area information comprises the preset driving area coordinate point;
judging whether the real-time coordinate position is in a cloud control intelligent driving area or not according to the real-time coordinate position;
if the real-time coordinate position is in the cloud control intelligent driving area, determining that the vehicle position is in the cloud control intelligent driving area in the driving area;
and if the real-time coordinate position is not in the cloud control intelligent driving area, determining that the vehicle position is in the single intelligent driving area.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations: before the step of obtaining the vehicle position and the preset coordinate point of the driving area, the method further comprises the following steps:
acquiring a global navigation satellite signal, and judging the signal intensity of the global navigation satellite signal;
and if the signal intensity is within a first preset intensity range, executing the step of acquiring the vehicle position and the preset coordinate point of the driving area.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations: after the step of obtaining the global navigation satellite signal and determining the signal strength of the global navigation satellite signal, the method further includes:
if the signal intensity is within a second preset intensity range, monitoring the position information of a preset marker and a vehicle in real time, wherein the second preset intensity range is smaller than the first preset intensity range;
when the vehicle is detected to reach the position of the preset marker, vehicle running information is acquired;
and switching the working mode according to the running information and the position information of the marker.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations: after the step of obtaining the global navigation satellite signal and determining the signal strength of the global navigation satellite signal, the method further includes:
when the signal intensity is within a third preset intensity range, controlling the road side equipment to acquire the current pose information of the vehicle, the current working mode of the vehicle and the surrounding environment of the vehicle, wherein the third preset intensity range is smaller than the second preset intensity range;
and switching the current working mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations: the step of switching the current working mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle comprises the following steps:
judging whether the vehicle is at risk of collision or not according to the speed of the vehicle, the acceleration of the vehicle, the position of the vehicle, the surrounding image of the vehicle and the distance between the vehicle and the obstacle, wherein the current pose information of the vehicle comprises the speed of the vehicle, the acceleration of the vehicle and the position of the vehicle, and the surrounding environment of the vehicle comprises the surrounding image of the vehicle and the distance between the vehicle and the obstacle;
if the vehicle has a collision risk and the current working mode is a single-vehicle intelligent driving mode, switching the single-vehicle intelligent driving mode into a cloud control intelligent driving mode;
and if the vehicle has no collision risk, switching the current working mode of the vehicle into a single intelligent driving mode.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations: the unmanned logistics vehicle control method comprises
When the vehicle working mode is in the cloud control intelligent driving mode, receiving a driving control instruction sent by the terminal equipment;
and controlling the vehicle to run and switching the current driving mode according to the driving control instruction.
In order to achieve the above object, the present invention also provides an unmanned logistics vehicle control apparatus, comprising:
the system comprises drive test equipment, an MEC device, a 5G base station, a core network, a cloud platform and terminal equipment which are sequentially in communication connection, wherein the 5G base station is also in communication connection with an unmanned logistics vehicle;
the drive test equipment comprises a camera, a laser radar and a drive test edge calculation unit;
the unmanned logistics vehicle comprises a 5G communication module, a single-vehicle intelligent driving controller module and a cloud-control intelligent driving controller module, wherein the single-vehicle intelligent driving controller module is connected with the cloud-control intelligent driving controller module through a CAN network.
In addition, in order to achieve the above object, the present invention further provides an unmanned logistics vehicle control system, which includes the unmanned logistics vehicle control method and the unmanned logistics vehicle control device, and a memory, a processor and an unmanned logistics vehicle control program stored in the memory and operable on the processor, wherein the unmanned logistics vehicle control program, when executed by the processor, implements the steps of the unmanned logistics vehicle control method.
In addition, in order to achieve the above object, the present invention further provides a readable storage medium, wherein the readable storage medium stores therein an unmanned logistics vehicle control program, and the unmanned logistics vehicle control program, when executed by a processor, implements the steps of the unmanned logistics vehicle control method as described above.
The invention provides a control method, a device and a system for an unmanned logistics vehicle and a readable storage medium, comprising the following steps: acquiring vehicle position information and driving area information, and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information; and switching the working mode of the unmanned logistics city according to the correlation between the vehicle position and the driving area. By the mode, the working mode of the unmanned logistics vehicle can be switched according to the driving area and the vehicle position, the unification of the driving area and the driving mode is ensured, the vehicle can be safely and reliably switched in the corresponding driving area, the intelligentization of vehicle driving is realized, the labor cost can be saved under the switching of the two modes, the accuracy of vehicle driving is kept, and the safe and reliable switching of the cloud control intelligent driving mode and the single vehicle intelligent driving mode is realized.
Drawings
FIG. 1 is a schematic diagram of an apparatus in a hardware operating environment according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a first embodiment of the control method of the unmanned logistics vehicle of the invention;
fig. 3 is a schematic flow chart of a control method for an unmanned logistics vehicle according to a second embodiment of the invention;
fig. 4 is a schematic flow chart of a fourth embodiment of the control method of the unmanned logistics vehicle of the invention;
fig. 5 is a schematic flow chart of a fifth embodiment of the unmanned logistics vehicle control method of the invention;
FIG. 6 is a schematic structural diagram of the unmanned logistics vehicle control system of the invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a computer, and can also be a mobile terminal device with a display function, such as a smart phone, a tablet computer, a portable computer and the like.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a DVI interface 1004, a USB interface 1005, and a memory 1006. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The DVI interface 1004 may optionally include a standard wired interface to connect with other external devices via DVI wires. The USB interface 1005 may optionally include a standard wired interface to connect with other external devices via a USB cable. The memory 1006 may be a high-speed RAM memory or a non-volatile memory (e.g., a disk memory). The memory 1006 may alternatively be a storage device separate from the processor 1001.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations: the terminal may also include audio circuitry and the like, which will not be described in detail herein.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, the memory 1006, which is a kind of computer storage medium, may include therein an operating system, a DVI interface module, a USB interface module, a user interface module, and an unmanned logistics vehicle control program.
In the terminal shown in fig. 1, the DVI interface 1004 is mainly used for connecting, and communicating data with, external devices; the USB interface 1005 is mainly used for connecting an external device and performing data communication with the external device; the user interface 1003 is mainly used for connecting a client and performing data communication with the client; and the processor 1001 may be configured to call the unattended physical distribution vehicle control program stored in the memory 1005, and perform the following operations:
acquiring vehicle position information and driving area information, and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information;
and switching the working mode of the unmanned logistics vehicle according to the correlation between the vehicle position and the driving area.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations:
the step of acquiring vehicle position information and driving area information and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information comprises the following steps:
acquiring a vehicle position and a preset driving area coordinate point, and determining a real-time coordinate position of the vehicle position in the preset coordinate point, wherein the vehicle position information comprises the vehicle position, and the driving area information comprises the preset driving area coordinate point;
judging whether the real-time coordinate position is in a cloud control intelligent driving area or not according to the real-time coordinate position;
if the real-time coordinate position is in the cloud control intelligent driving area, determining that the vehicle position is in the cloud control intelligent driving area in the driving area;
and if the real-time coordinate position is not in the cloud control intelligent driving area, determining that the vehicle position is in the single intelligent driving area.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations:
before the step of obtaining the vehicle position and the preset coordinate point of the driving area, the method further comprises the following steps:
acquiring a global navigation satellite signal, and judging the signal intensity of the global navigation satellite signal;
and if the signal intensity is within a first preset intensity range, executing the step of acquiring the vehicle position and the preset coordinate point of the driving area.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations:
after the step of obtaining the global navigation satellite signal and determining the signal strength of the global navigation satellite signal, the method further includes:
if the signal intensity is within a second preset intensity range, monitoring the position information of a preset marker and a vehicle in real time, wherein the second preset intensity range is smaller than the first preset intensity range;
when the vehicle is detected to reach the position of the preset marker, vehicle running information is acquired;
and switching the working mode according to the running information and the position information of the marker.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations:
after the step of obtaining the global navigation satellite signal and determining the signal strength of the global navigation satellite signal, the method further includes:
when the signal intensity is within a third preset intensity range, controlling the road side equipment to acquire the current pose information of the vehicle, the current working mode of the vehicle and the surrounding environment of the vehicle, wherein the third preset intensity range is smaller than the second preset intensity range;
and switching the current working mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations:
the step of switching the current working mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle comprises the following steps:
judging whether the vehicle is at risk of collision or not according to the speed of the vehicle, the acceleration of the vehicle, the position of the vehicle, the surrounding image of the vehicle and the distance between the vehicle and the obstacle, wherein the current pose information of the vehicle comprises the speed of the vehicle, the acceleration of the vehicle and the position of the vehicle, and the surrounding environment of the vehicle comprises the surrounding image of the vehicle and the distance between the vehicle and the obstacle;
if the vehicle has a collision risk and the current working mode is a single-vehicle intelligent driving mode, switching the single-vehicle intelligent driving mode into a cloud control intelligent driving mode;
and if the vehicle has no collision risk, switching the current working mode of the vehicle into a single intelligent driving mode.
Further, the processor 1001 may call the unattended physical distribution vehicle control program stored in the memory 1006, and further perform the following operations:
when the vehicle working mode is in the cloud control intelligent driving mode, receiving a driving control instruction sent by the terminal equipment;
and controlling the vehicle to run and switching the current driving mode according to the driving control instruction.
Referring to fig. 6, in an embodiment, the unmanned logistics vehicle control system further includes:
the system comprises drive test equipment, an MEC device 02, a 5G base station 03, a core network 04, a cloud platform 06 and terminal equipment 07 which are sequentially in communication connection, wherein the 5G base station 03 is also in communication connection with an unmanned logistics vehicle 05; the drive test equipment comprises a camera 011, a laser radar 012 and a roadside edge calculation unit 013; unmanned commodity circulation car 05 includes 5G communication module 051, bicycle intelligent driving module 052 and cloud accuse intelligent driving module 053, wherein, bicycle intelligent driving module 052 with pass through CAN internet connection between the cloud accuse intelligent driving module 053.
In this embodiment, the roadside apparatus 01 is arranged at the roadside and is used for acquiring the current pose information of the vehicle, the current working mode of the vehicle and the surrounding environment of the vehicle; the MEC device 02 processes the relevant information data acquired by the roadside equipment 01 and sends the processed relevant information data to the cloud platform 06; the 5G base station 03 and the core network 04 are configured to communicate data; the roadside equipment 01 is connected with the MEC device 02 through a special optical fiber line, and the MEC device 02 is connected with the 5G base station 03 through a 5G network; the 5G base station 03 is connected with the unmanned logistics vehicle 05 through a 5G network; the 5G base station 03 is connected with the core network 04 and the core network 04 is connected with the cloud platform 06 through optical fibers; the cloud platform 06 and the terminal device 07 are connected through a 5G signal. Through the device and the connection mode, the unmanned logistics vehicle 05 can receive a driving control command of the terminal device 07, and can switch the working mode according to the data collected by the road side device 01, so that the safety and the reliability in the mode switching process can be ensured.
The specific embodiment of the unmanned logistics vehicle control system of the invention is basically the same as the following embodiments of the unmanned logistics vehicle control method, and is not described herein again.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the method for controlling an unmanned logistics vehicle according to the present invention, and the method for controlling an unmanned logistics vehicle provided by the present embodiment includes the following steps:
step S10, obtaining vehicle position information and driving area information, and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information;
in this embodiment, the vehicle position information includes a vehicle position, the driving area information includes a driving area, and the vehicle position information may be acquired according to a vehicle-mounted navigation device, such as a GPS navigation device, a beidou navigation device, and the like, which is not limited herein. The driving area comprises a cloud control intelligent driving area and a single-vehicle intelligent driving area, and specifically, the two areas can be divided by setting different coordinate points in the two areas, wherein the cloud control driving area can be an area with complex terrain, and the single-vehicle intelligent driving area can be an area with simple terrain, and the specific division basis is not limited herein.
Step S20, switching the working mode of the unmanned logistics vehicle according to the correlation between the vehicle position and the driving area;
in this embodiment, the working modes of the unmanned logistics vehicle comprise a cloud control intelligent driving mode and a single vehicle intelligent driving mode, wherein the cloud control intelligent driving mode is that the vehicle is controlled and operated manually through communication connection; the intelligent driving mode of the single vehicle is that the vehicle senses the environment through a sensor of the vehicle and controls the automatic operation of the vehicle through the environment sensing. The switching of the working mode of the unmanned logistics vehicle according to the correlation between the vehicle position and the driving area is that when the vehicle position is in the cloud control intelligent driving area, the working mode of the unmanned logistics vehicle is switched to the cloud control intelligent driving mode, and when the vehicle position is in the single vehicle intelligent driving area, the working mode of the unmanned logistics vehicle is switched to the single vehicle intelligent driving mode.
The invention provides a control method of an unmanned logistics vehicle, which comprises the following steps: acquiring vehicle position information and driving area information, and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information; and switching the working mode of the unmanned logistics city according to the correlation between the vehicle position and the driving area. By the mode, the working mode of the unmanned logistics vehicle can be switched according to the driving area and the vehicle position, the unification of the driving area and the driving mode is ensured, the vehicle can be safely and reliably switched in the corresponding driving area, the intelligentization of vehicle driving is realized, the labor cost can be saved under the switching of the two modes, the accuracy of vehicle driving is kept, and the safe and reliable switching of the cloud control intelligent driving mode and the single vehicle intelligent driving mode is realized.
Further, referring to fig. 3, a second embodiment of the unmanned logistics vehicle control method according to the present invention provides an unmanned logistics vehicle control method, based on the embodiment shown in fig. 2, the step of acquiring vehicle position information and driving area information, and determining a correlation between a vehicle position and a driving area according to the vehicle position information and the driving area information includes:
step S11, acquiring a vehicle position and a driving area preset coordinate point, and determining a real-time coordinate position of the vehicle position in the preset coordinate point, wherein the vehicle position information comprises the vehicle position, and the driving area information comprises the driving area preset coordinate point;
in this embodiment, the driving area preset coordinate points include a single-vehicle intelligent driving area preset coordinate point and a cloud-control intelligent driving area preset coordinate point, specifically, an area with a complex terrain can be divided into a cloud-control intelligent driving area, and the preset coordinate points are allocated, or an area with a large pedestrian volume is divided into a cloud-control intelligent driving area; the method comprises the steps of dividing a simple terrain area into a single-vehicle intelligent driving area, and distributing preset coordinate points, or dividing an area with small pedestrian volume into the single-vehicle intelligent driving area. In addition, in the embodiment, a real-time human body infrared distribution image within a preset range can be obtained, and when the human body infrared area in the human body infrared distribution image is gathered, the area is divided into a bicycle intelligent driving area; when the human body infrared area is dispersed, the area is divided into a cloud control intelligent driving area.
Step S12, judging whether the real-time coordinate position is in a cloud-control intelligent driving area or not according to the real-time coordinate position;
step S13, if the real-time coordinate position is in the cloud-controlled intelligent driving area, determining that the vehicle position is in the cloud-controlled intelligent driving area in the driving area;
step S14, if the real-time coordinate position of the searched fox is not in the cloud-control intelligent driving area, the vehicle position is judged to be in the single-vehicle intelligent driving area;
in the embodiment, when the vehicle is in a cloud control intelligent driving area, the working mode of the unmanned logistics vehicle is switched to a cloud control intelligent driving mode, the control instruction of the terminal device is received, and driving and operation are carried out according to the control instruction; when the vehicle is in the single-vehicle intelligent driving area, the working mode of the unmanned logistics vehicle is switched to the single-vehicle intelligent driving mode, so that the unmanned logistics vehicle can automatically run and operate according to the surrounding environment.
According to the invention, the vehicle position and the driving area can be accurately identified by acquiring the preset coordinate points of the vehicle position and the driving area and determining the real-time coordinate position of the vehicle position in the preset coordinate points, so that the accuracy of working mode switching is ensured; whether the real-time coordinate position is in a cloud control intelligent driving area or not is judged according to the real-time coordinate position, if the real-time coordinate position is in the cloud control intelligent driving area, the vehicle position is judged to be in the cloud control intelligent driving area in the driving area, and if the real-time coordinate position is not in the cloud control intelligent driving area, the vehicle position is judged to be in a single vehicle meeting intelligent driving area, so that the change of the vehicle working mode along with the switching of the driving area is realized, the safety and the reliability of the switching of the vehicle working mode are ensured, meanwhile, the labor cost can be saved, the intellectualization of the unmanned logistics vehicle is improved through the switching of the two modes, and the working efficiency is ensured.
Further, a third embodiment of the method for controlling an unmanned logistics vehicle according to the present invention provides a method for controlling an unmanned logistics vehicle, where based on the embodiment shown in fig. 2, before the step of obtaining the vehicle position and the preset coordinate point of the driving area, the method further includes:
acquiring a global navigation satellite signal, and judging the signal intensity of the global navigation satellite signal;
if the signal intensity is within a first preset intensity range, executing: step S11;
in this embodiment, the global navigation satellite signal may be a GPS signal or a beidou navigation signal, and the present invention is not limited herein, and in the first preset range, the unmanned logistics vehicle can keep the accuracy of obtaining the vehicle position and the preset coordinate point to be the strongest, and the numerical value of the first preset range is not limited herein, and a person skilled in the art can set the numerical value according to actual needs. When the signal strength is within the first preset range, step S11 is executed to switch the operation mode according to the driving area and the vehicle position. In this embodiment, the switching condition of the vehicle working mode is determined by determining the signal strength of the global navigation satellite signal, so that the safety and reliability of the vehicle working mode switching can be better ensured, and the working efficiency of the unmanned logistics vehicle is improved.
Further, referring to fig. 4, a fourth embodiment of the unmanned logistics vehicle control method according to the present invention provides an unmanned logistics vehicle control method, based on the embodiment shown in fig. 2, after the step of acquiring a global navigation satellite signal and determining a signal strength of the global navigation satellite signal, the method further includes:
step S30, if the signal intensity is in a second preset intensity range, monitoring the position information of a preset marker and a vehicle in real time, wherein the second preset intensity range is smaller than the first preset intensity range;
in this embodiment, when the signal intensity is within the second preset intensity range, the accuracy of the unmanned logistics vehicle for acquiring the vehicle position and the preset coordinate point is poor, so as to avoid the influence on the work of the unmanned logistics vehicle due to the strength of the signal intensity; the marker is a facility for distinguishing a driving pattern of the area, such as a guideboard, a landmark building, a tree, and the like.
Step S40, when the vehicle is detected to reach the position of the preset marker, vehicle running information is obtained;
step S50, switching the working mode according to the running information and the position information of the marker;
in the present embodiment, the vehicle travel information includes a vehicle travel route and a vehicle travel speed; after the vehicle reaches the position of the preset marker, judging whether the remaining driving process is in a complex terrain area or a simple terrain area according to the driving route, and switching the working mode to a cloud control intelligent driving area when the area in which the remaining driving process is located belongs to the complex terrain area; when the area where the rest driving process is located belongs to a simple terrain area, the working mode is switched to a single-vehicle intelligent driving area; or in combination with the driving area described in the above embodiment, when the remaining driving process is in the cloud-controlled intelligent driving area, the working mode is controlled to be switched to the cloud-controlled intelligent driving mode, and when the remaining driving process is in the single-vehicle intelligent driving area, the working mode is switched to the single-vehicle intelligent driving mode. In addition, in this embodiment, it may be further detected that the vehicle reaches the position range where the preset marker is located, and vehicle driving information is acquired.
In the invention, when the signal intensity is in a second preset intensity range, the position information of the preset marker and the vehicle is monitored in real time, and after the vehicle is detected to reach the position of the preset marker, the working mode is switched according to the vehicle running information and the position information of the marker, so that the switching of the vehicle mode under the weak navigation signal is realized, the driving mode can be accurately switched by setting the marker, and the problem of conflict between the single-vehicle intelligent driving mode and the cloud-control intelligent driving mode under the weak navigation signal is solved.
Further, referring to fig. 5, a fifth embodiment of the unmanned logistics vehicle control method according to the present invention provides an unmanned logistics vehicle control method, based on the embodiment shown in fig. 2, after the step of acquiring a global navigation satellite signal and determining a signal strength of the global navigation satellite signal, the method further includes:
step S60, when the signal intensity is within a third preset intensity range, controlling the road side equipment to acquire the current pose information of the vehicle, the current working mode of the vehicle and the surrounding environment of the vehicle, wherein the third preset intensity range is smaller than the second preset intensity range;
in this embodiment, when the signal intensity is within the third preset intensity range, if the unmanned logistics vehicle cannot acquire the vehicle position and the preset coordinate point, the roadside device is controlled to acquire the current pose information of the vehicle, where the vehicle pose information is the position information and the posture information of the vehicle, and it needs to be noted that the roadside device includes a camera, a laser radar, and a roadside edge calculation unit.
And step S70, switching the current working mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle.
Specifically, in an embodiment, the step S70 includes:
step a71, based on a speed of a vehicle, an acceleration of the vehicle, a position of the vehicle, a surrounding environment of the vehicle, and a distance of the vehicle from an obstacle. Judging whether the vehicle is at a collision risk or not, wherein the current pose information of the vehicle comprises the speed of the vehicle, the acceleration of the vehicle and the position of the vehicle, and the surrounding environment of the vehicle comprises a surrounding environment image of the vehicle and the distance between the vehicle and an obstacle;
in this embodiment, whether there is a collision risk between the vehicle and an obstacle may be determined according to the speed, acceleration, position of the vehicle and the obstacle detected during the driving of the vehicle, where the obstacle includes people, buildings, vehicles, animals, and other objects that hinder the movement of the vehicle, and the invention is not limited herein.
Step A72, if the vehicle has a collision risk and the current working mode is a single-vehicle intelligent driving mode, switching the single-vehicle intelligent driving mode into a cloud control intelligent driving mode;
in this embodiment, when the vehicle has a collision risk, it is described that human intervention is required to prevent the vehicle from colliding when the vehicle has the collision risk, and the intelligent driving mode of the single vehicle needs to be switched to the cloud control intelligent driving mode.
Step A73, if the vehicle has no collision risk, switching the current working mode of the vehicle into a single-vehicle intelligent driving mode;
in the embodiment, under the condition that the vehicle has no collision risk, the current working mode of the vehicle can be switched into the single intelligent driving mode because human intervention is not needed.
According to the method, whether the vehicle has a collision risk is judged according to the speed of the vehicle, the acceleration of the vehicle, the position of the vehicle, the image of the surrounding environment of the vehicle and the distance between the vehicle and an obstacle, and when the vehicle has the collision risk and the current working mode is the intelligent driving mode of the single vehicle, the intelligent driving mode of the single vehicle is switched to the intelligent driving mode of cloud control; if the vehicle has no collision risk, the current working mode of the vehicle is switched to a single intelligent driving mode, so that the safety of the vehicle in the driving process can be improved, and the occurrence of collision accidents is avoided.
Further, a sixth embodiment of the method for controlling an unmanned logistics vehicle according to the present invention provides a method for controlling an unmanned logistics vehicle, which is based on the above embodiment shown in fig. 2 and includes the steps of
Step a, when the vehicle working mode is in a cloud control intelligent driving mode, receiving a driving control instruction sent by terminal equipment;
and b, controlling the vehicle to run and switching the current driving mode according to the driving control instruction.
In this embodiment, the terminal device may be a mobile device such as a mobile phone and a tablet, and specifically, the driving control instruction may be sent through an APP on the mobile device, where the driving control instruction may be a mode switching instruction or a control instruction in a driving process, such as a control instruction for parking, advancing, steering, and the like. Specifically, the vehicle may run or switch the current driving instruction according to the driving control instruction. In addition, when the vehicle working mode is in the single intelligent driving mode, the driving control instruction can be received to switch the modes.
In the invention, when the working mode of the vehicle is in the cloud control intelligent driving mode, the driving control instruction sent by the terminal equipment is received, the vehicle is controlled to run and the current driving mode is switched according to the driving control instruction, the initiative of vehicle mode switching can be ensured, the working mode of the vehicle can be switched autonomously, and the safety of the vehicle in the running process is improved.
In addition, an embodiment of the present invention further provides a readable storage medium, where the readable storage medium stores an unmanned logistics vehicle control program, and the unmanned logistics vehicle control program, when executed by a processor, implements the following operations:
acquiring vehicle position information and driving area information, and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information;
and switching the working mode of the unmanned logistics vehicle according to the correlation between the vehicle position and the driving area.
Further, the unmanned logistics vehicle control program further realizes the following operations when executed by the processor:
the step of acquiring vehicle position information and driving area information and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information comprises the following steps:
acquiring a vehicle position and a preset driving area coordinate point, and determining a real-time coordinate position of the vehicle position in the preset coordinate point, wherein the vehicle position information comprises the vehicle position, and the driving area information comprises the preset driving area coordinate point;
judging whether the real-time coordinate position is in a cloud control intelligent driving area or not according to the real-time coordinate position;
if the real-time coordinate position is in the cloud control intelligent driving area, determining that the vehicle position is in the cloud control intelligent driving area in the driving area;
and if the real-time coordinate position is not in the cloud control intelligent driving area, determining that the vehicle position is in the single intelligent driving area.
Further, the unmanned logistics vehicle control program further realizes the following operations when executed by the processor:
before the step of obtaining the vehicle position and the preset coordinate point of the driving area, the method further comprises the following steps:
acquiring a global navigation satellite signal, and judging the signal intensity of the global navigation satellite signal;
and if the signal intensity is within a first preset intensity range, executing the step of acquiring the vehicle position and the preset coordinate point of the driving area.
Further, the unmanned logistics vehicle control program further realizes the following operations when executed by the processor:
after the step of obtaining the global navigation satellite signal and determining the signal strength of the global navigation satellite signal, the method further includes:
if the signal intensity is within a second preset intensity range, monitoring the position information of a preset marker and a vehicle in real time, wherein the second preset intensity range is smaller than the first preset intensity range;
when the vehicle is detected to reach the position of the preset marker, vehicle running information is acquired;
and switching the working mode according to the running information and the position information of the marker.
Further, the unmanned logistics vehicle control program further realizes the following operations when executed by the processor:
after the step of obtaining the global navigation satellite signal and determining the signal strength of the global navigation satellite signal, the method further includes:
when the signal intensity is within a third preset intensity range, controlling the road side equipment to acquire the current pose information of the vehicle, the current working mode of the vehicle and the surrounding environment of the vehicle, wherein the third preset intensity range is smaller than the second preset intensity range;
and switching the current working mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle.
Further, the unmanned logistics vehicle control program further realizes the following operations when executed by the processor:
the step of switching the current working mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle comprises the following steps:
judging whether the vehicle is at risk of collision or not according to the speed of the vehicle, the acceleration of the vehicle, the position of the vehicle, the surrounding image of the vehicle and the distance between the vehicle and the obstacle, wherein the current pose information of the vehicle comprises the speed of the vehicle, the acceleration of the vehicle and the position of the vehicle, and the surrounding environment of the vehicle comprises the surrounding image of the vehicle and the distance between the vehicle and the obstacle;
if the vehicle has a collision risk and the current working mode is a single-vehicle intelligent driving mode, switching the single-vehicle intelligent driving mode into a cloud control intelligent driving mode;
and if the vehicle has no collision risk, switching the current working mode of the vehicle into a single intelligent driving mode.
Further, the unmanned logistics vehicle control program further realizes the following operations when executed by the processor: the unmanned logistics vehicle control method comprises
When the vehicle working mode is in the cloud control intelligent driving mode, receiving a driving control instruction sent by the terminal equipment;
and controlling the vehicle to run and switching the current driving mode according to the driving control instruction. The specific embodiment of the readable storage medium of the present invention is basically the same as the embodiments of the above-mentioned unmanned logistics vehicle control method, and is 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 system 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 system. 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 system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A control method for an unmanned logistics vehicle is characterized by comprising the following steps:
acquiring vehicle position information and driving area information, and judging the correlation between the vehicle position and the driving area according to the vehicle position information and the driving area information;
and switching the working mode of the unmanned logistics vehicle according to the correlation between the vehicle position and the driving area.
2. The unmanned logistics vehicle control method of claim 1, wherein the step of obtaining vehicle position information and driving area information and determining the correlation between the vehicle position and the driving area based on the vehicle position information and the driving area information comprises:
acquiring a vehicle position and a preset driving area coordinate point, and determining a real-time coordinate position of the vehicle position in the preset coordinate point, wherein the vehicle position information comprises the vehicle position, and the driving area information comprises the preset driving area coordinate point;
judging whether the real-time coordinate position is in a cloud control intelligent driving area or not according to the real-time coordinate position;
if the real-time coordinate position is in the cloud control intelligent driving area, determining that the vehicle position is in the cloud control intelligent driving area in the driving area;
and if the real-time coordinate position is not in the cloud control intelligent driving area, determining that the vehicle position is in the single intelligent driving area.
3. The unmanned logistics vehicle control method of claim 2, wherein the step of obtaining the vehicle position and the driving area preset coordinate point is preceded by the step of:
acquiring a global navigation satellite signal, and judging the signal intensity of the global navigation satellite signal;
and if the signal intensity is within a first preset intensity range, executing the step of acquiring the vehicle position and the preset coordinate point of the driving area.
4. The method for controlling an unmanned logistics vehicle of claim 3, wherein after the step of acquiring global navigation satellite signals and determining the signal strength of the global navigation satellite signals, the method further comprises:
if the signal intensity is within a second preset intensity range, monitoring the position information of a preset marker and a vehicle in real time, wherein the second preset intensity range is smaller than the first preset intensity range;
when the vehicle is detected to reach the position of the preset marker, vehicle running information is acquired;
and switching the working mode according to the running information and the position information of the marker.
5. The method for controlling an unmanned logistics vehicle of claim 4, wherein after the step of acquiring global navigation satellite signals and determining the signal strength of the global navigation satellite signals, the method further comprises:
when the signal intensity is within a third preset intensity range, controlling the road side equipment to acquire the current pose information of the vehicle, the current working mode of the vehicle and the surrounding environment of the vehicle, wherein the third preset intensity range is smaller than the second preset intensity range;
and switching the current working mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle.
6. The unmanned logistics vehicle control method of claim 5, wherein the step of switching the current operating mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle comprises:
judging whether the vehicle is at risk of collision or not according to the speed of the vehicle, the acceleration of the vehicle, the position of the vehicle, the surrounding image of the vehicle and the distance between the vehicle and the obstacle, wherein the current pose information of the vehicle comprises the speed of the vehicle, the acceleration of the vehicle and the position of the vehicle, and the surrounding environment of the vehicle comprises the surrounding image of the vehicle and the distance between the vehicle and the obstacle;
if the vehicle has a collision risk and the current working mode is a single-vehicle intelligent driving mode, switching the single-vehicle intelligent driving mode into a cloud control intelligent driving mode;
and if the vehicle has no collision risk, switching the current working mode of the vehicle into a single intelligent driving mode.
7. The unmanned logistics vehicle control method of any one of claims 1 to 6, wherein the unmanned logistics vehicle control method comprises:
when the vehicle working mode is in the cloud control intelligent driving mode, receiving a driving control instruction sent by the terminal equipment;
and controlling the vehicle to run and switching the current driving mode according to the driving control instruction.
8. An unmanned logistics vehicle control device, comprising:
the system comprises drive test equipment, an MEC device, a 5G base station, a core network, a cloud platform and terminal equipment which are sequentially in communication connection, wherein the 5G base station is also in communication connection with an unmanned logistics vehicle;
the drive test equipment comprises a camera, a laser radar and a drive test edge calculation unit;
the unmanned logistics vehicle comprises a 5G communication module, a single-vehicle intelligent driving controller module and a cloud-control intelligent driving controller module, wherein the single-vehicle intelligent driving controller module is connected with the cloud-control intelligent driving controller module through a CAN network.
9. An unmanned logistics vehicle control system, characterized in that the unmanned logistics vehicle control system comprises the unmanned logistics vehicle control method and the unmanned logistics vehicle control device, and a memory, a processor and an unmanned logistics vehicle control program stored on the memory and operable on the processor, wherein the unmanned logistics vehicle control program when executed by the processor implements the steps of the unmanned logistics vehicle control method according to any one of claims 1 to 7.
10. A readable storage medium, wherein the readable storage medium has stored thereon an unmanned logistics vehicle control program, which when executed by a processor, implements the steps of the unmanned logistics vehicle control method of any one of claims 1 to 7.
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