CN114228743B - 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 PDF

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Publication number
CN114228743B
CN114228743B CN202111447760.3A CN202111447760A CN114228743B CN 114228743 B CN114228743 B CN 114228743B CN 202111447760 A CN202111447760 A CN 202111447760A CN 114228743 B CN114228743 B CN 114228743B
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vehicle
driving area
preset
information
unmanned logistics
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CN114228743A (en
Inventor
谢燕萍
莫志敏
潘涛
何逸波
陆宁徽
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SAIC GM Wuling Automobile Co Ltd
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SAIC GM Wuling Automobile Co Ltd
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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
    • 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
    • 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
    • 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 or light sensitive means, e.g. infrared sensors
    • B60W2420/403—Image sensing, e.g. optical camera
    • B60W2420/408—
    • 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
    • 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 for navigation systems
    • 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]

Abstract

The invention discloses an unmanned logistics vehicle control method, device and system 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 mutual relation 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

Unmanned logistics vehicle control method, device and system and readable storage medium
Technical Field
The invention relates to the technical field of intelligent driving, in particular to an unmanned logistics vehicle control method, an unmanned logistics vehicle control device, an unmanned logistics vehicle control system and a readable storage medium.
Background
With the development of intelligent driving technology, the safety requirements on intelligent driving of vehicles are higher and higher. The current intelligent driving comprises a bicycle intelligent driving mode, a cloud control driving mode and a driving mode, wherein the bicycle intelligent driving mode mainly carries out environment sensing, calculation decision making and control execution by means of sensors such as a vehicle vision sensor, a millimeter wave radar sensor, a laser radar sensor and the like, but a plurality of links of environment sensing, calculation decision making and control execution have technical bottlenecks with different degrees at present, and various failure problems are easy to occur in the application process; cloud control intelligent driving organically links the human-vehicle-road-cloud traffic participation elements together through the Internet of vehicles on the basis of a single vehicle intelligent driving mode, and expands and assists the capability 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, two modes exist, so that the problem of conflict of the two modes can occur 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 an unmanned logistics vehicle control method, device and system and a readable storage medium. The method aims at solving the problem that the prior automatic driving mode of the vehicle cannot be switched safely and reliably in the switching process.
In order to achieve the above purpose, the invention provides an unmanned logistics vehicle control method, 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 mutual relation between the vehicle position and the driving area.
Further, the processor 1001 may call the unmanned logistics 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 interrelation 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 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;
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, judging 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, judging that the vehicle position is in the single vehicle intelligent driving area.
Further, the processor 1001 may call the unmanned logistics vehicle control program stored in the memory 1006, and further perform the following operations: before the step of obtaining the preset coordinate points of the vehicle position and 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 in the first preset intensity range, executing the step of acquiring the preset coordinate points of the vehicle position and the driving area.
Further, the processor 1001 may call the unmanned logistics 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 judging the signal strength of the global navigation satellite signal, the method further comprises the following steps:
if the signal intensity is in a second preset intensity range, monitoring the position information of the preset marker and the 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, acquiring vehicle running information;
and switching the working modes according to the running information and the position information of the marker.
Further, the processor 1001 may call the unmanned logistics 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 judging the signal strength of the global navigation satellite signal, the method further comprises the following steps:
when the signal intensity is in a third preset intensity range, controlling road side equipment to acquire current pose information of the vehicle, a current working mode of the vehicle and 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 unmanned logistics 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 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 an 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 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-vehicle intelligent driving mode.
Further, the processor 1001 may call the unmanned logistics vehicle control program stored in the memory 1006, and further perform the following operations: the unmanned logistics vehicle control method comprises the following steps of
When the vehicle working mode is in the cloud control intelligent driving mode, a driving control instruction sent by a terminal device is received;
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 further 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 road test equipment comprises a camera, a laser radar and a road test edge calculation unit;
the unmanned logistics vehicle comprises a 5G communication module, a bicycle intelligent driving controller module and a cloud control intelligent driving controller module, wherein the bicycle 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 purpose, the present invention also provides an unmanned logistics vehicle control system, which includes the unmanned logistics vehicle control method, the unmanned logistics vehicle control device, a memory, a processor, and an unmanned logistics vehicle control program stored in the memory and capable of running 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, on which an unmanned logistics vehicle control program is stored, which when executed by a processor, implements the steps of the unmanned logistics vehicle control method as described above.
The invention provides an unmanned logistics vehicle control method, device and system and a readable storage medium, which comprise 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 mutual relation between the vehicle position and the driving area. Through the mode, the working modes 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 switched safely and reliably in the corresponding driving area, the intellectualization of the vehicle driving is realized, the labor cost can be saved under the switching of the two modes, meanwhile, the accuracy of the vehicle driving is maintained, 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 a device architecture of 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 unmanned logistics vehicle control method of the present invention;
FIG. 3 is a schematic flow chart of a second embodiment of the unmanned logistics vehicle control method of the present invention;
FIG. 4 is a schematic flow chart of a fourth embodiment of the unmanned logistics vehicle control method of the present invention;
FIG. 5 is a schematic flow chart of a fifth embodiment of the unmanned logistics vehicle control method of the present invention;
FIG. 6 is a schematic diagram of the unmanned logistics vehicle control system of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, fig. 1 is a schematic diagram of a terminal structure of a hardware running environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a computer, and also can be mobile terminal equipment with a display function, such as a smart phone, a tablet personal 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 the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a 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 lines. The USB interface 1005 may optionally include a standard wired interface, which connects to other external devices via a USB connection. The memory 1006 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1006 may also optionally be a storage device separate from the processor 1001 described above.
Further, the processor 1001 may call the unmanned logistics 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 are not described in detail herein.
It will be appreciated by those skilled in the art that the terminal structure shown in fig. 1 is not limiting of the terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a DVI interface module, a USB interface module, a user interface module, and an unmanned logistics vehicle control program may be included in the memory 1006 as one type of computer storage medium.
In the terminal shown in fig. 1, the DVI interface 1004 is mainly used for connecting an external device, and performing data communication with the external device; 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 communicating data with the client; and the processor 1001 may be configured to call the unmanned logistics 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 mutual relation between the vehicle position and the driving area.
Further, the processor 1001 may call the unmanned logistics 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 interrelation 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 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;
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, judging 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, judging that the vehicle position is in the single vehicle intelligent driving area.
Further, the processor 1001 may call the unmanned logistics vehicle control program stored in the memory 1006, and further perform the following operations:
before the step of obtaining the preset coordinate points of the vehicle position and 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 in the first preset intensity range, executing the step of acquiring the preset coordinate points of the vehicle position and the driving area.
Further, the processor 1001 may call the unmanned logistics 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 judging the signal strength of the global navigation satellite signal, the method further comprises the following steps:
if the signal intensity is in a second preset intensity range, monitoring the position information of the preset marker and the 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, acquiring vehicle running information;
and switching the working modes according to the running information and the position information of the marker.
Further, the processor 1001 may call the unmanned logistics 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 judging the signal strength of the global navigation satellite signal, the method further comprises the following steps:
when the signal intensity is in a third preset intensity range, controlling road side equipment to acquire current pose information of the vehicle, a current working mode of the vehicle and 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 unmanned logistics 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 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 an 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 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-vehicle intelligent driving mode.
Further, the processor 1001 may call the unmanned logistics 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, a driving control instruction sent by a terminal device is received;
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 comprises:
the system comprises drive test equipment, MEC devices 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 road test device comprises a camera 011, a laser radar 012 and a road side edge calculation unit 013; the unmanned logistics vehicle 05 comprises a 5G communication module 051, a bicycle intelligent driving module 052 and a cloud control intelligent driving module 053, wherein the bicycle intelligent driving module 052 is connected with the cloud control intelligent driving module 053 through a CAN network.
In this embodiment, the roadside apparatus 01 is disposed at a roadside, and is configured to obtain current pose information of a vehicle, a current working mode of the vehicle, and a surrounding environment of the vehicle; the MEC device 02 processes the related information data acquired by the road side equipment 01 and sends the related information data to the cloud platform 06; the 5G base station 03 and the core network 04 are used for transmitting data; the road side equipment 01 is connected with the MEC device 02 through an optical fiber private 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 is connected with the terminal device 07 through a 5G signal. Through above-mentioned device and connected mode, can make unmanned commodity circulation car 05 receive terminal equipment 07's driving control command to and according to the switching of mode is carried out to the data that road side equipment 01 gathered, can guarantee the security and then the reliability in the mode switching process.
The specific embodiment of the control system of the unmanned logistics vehicle is basically the same as the following embodiments of the control method of the unmanned logistics vehicle, and is not described herein.
Referring to fig. 2, fig. 2 is a flow chart of a first embodiment of the control method of the unmanned logistics vehicle according to the present invention, and the control method of the unmanned logistics vehicle according to the present embodiment includes the following steps:
step S10, acquiring vehicle position information and driving area information, and judging the interrelation 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, for example, a GPS navigation device, a beidou navigation device, or the like, and the present invention is not limited herein. The driving area comprises a cloud control intelligent driving area and a bicycle 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, the bicycle intelligent driving area can be an area with simpler terrain, and the invention is not limited herein.
Step S20, switching the working mode of the unmanned logistics vehicle according to the mutual relation between the vehicle position and the driving area;
in this embodiment, the operation modes of the unmanned logistics vehicle include a cloud control intelligent driving mode and a single vehicle intelligent driving mode, where the cloud control intelligent driving mode is to manually control the vehicle through communication connection; the intelligent bicycle driving mode is that the vehicle carries out environment sensing through a sensor of the vehicle, and then the vehicle is automatically controlled to run through the environment sensing. According to the mutual relation between the vehicle position and the driving area, the working mode of the unmanned logistics vehicle is switched, namely, 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 an unmanned logistics vehicle control method, 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 mutual relation between the vehicle position and the driving area. Through the mode, the working modes 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 switched safely and reliably in the corresponding driving area, the intellectualization of the vehicle driving is realized, the labor cost can be saved under the switching of the two modes, meanwhile, the accuracy of the vehicle driving is maintained, 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 control method for an unmanned logistics vehicle according to the present invention provides a control method for an unmanned logistics vehicle, based on the embodiment shown in fig. 2, the step of obtaining 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 preset coordinate points of the driving area include a preset coordinate point of a bicycle intelligent driving area and a preset coordinate point of a cloud control intelligent driving area, specifically, an area with complex terrain may be divided into cloud control intelligent driving areas, and preset coordinate points are allocated, or an area with large traffic is divided into cloud control intelligent driving areas; the method comprises the steps of dividing a region with simple terrain into a bicycle intelligent driving region, and distributing preset coordinate points, or dividing a region with small traffic into the bicycle intelligent driving region. In addition, in the embodiment, a real-time human infrared distribution image in a preset range can be acquired, and when the human infrared areas are gathered in the human infrared distribution image, the area is divided into a bicycle intelligent driving area; when the infrared areas of the human bodies are scattered, the area is divided into cloud control intelligent driving areas.
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 control intelligent driving area, judging that the vehicle position is in the cloud control intelligent driving area in the driving area;
step S14, if the real-time coordinate position of the searching fox is not in the cloud control intelligent driving area, judging that the vehicle position is in a bicycle intelligent driving area;
in the embodiment, when the vehicle 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 a control instruction of the terminal equipment is received, and running and operation are performed according to the control instruction; when the vehicle is in the intelligent driving area of the bicycle, the working mode of the unmanned logistics vehicle is switched to the intelligent driving mode of the bicycle, 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 switching the working modes is ensured; 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, judging that the vehicle position is in the cloud control intelligent driving area in the driving area, if the real-time coordinate position is not in the cloud control intelligent driving area, judging that the vehicle position is in a bicycle meeting intelligent driving area, realizing that the vehicle working mode changes along with the switching of the driving area, ensuring the safety and the reliability of the switching of the vehicle working mode, simultaneously, saving the labor cost and improving the intellectualization of the unmanned logistics vehicle through the switching of the two modes, and ensuring the working efficiency.
Further, a third embodiment of the present invention provides a method for controlling an unmanned logistics vehicle, based on the embodiment shown in fig. 2, before the step of obtaining the preset coordinate points of the vehicle position and 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 strength is within the first preset strength range, executing: step S11;
in this embodiment, the global navigation satellite signal may be a GPS signal or a beidou navigation signal, etc., which is not limited herein, and in the first preset range, the unmanned logistics vehicle can keep the highest accuracy of acquiring the vehicle position and the preset coordinate point, and the numerical value of the first preset range is not limited herein, and can be set by those skilled in the art according to actual needs. And when the signal intensity is within the first preset range, executing step S11, and switching the working modes 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 present invention provides an unmanned logistics vehicle control method, based on the embodiment shown in fig. 2, after the step of obtaining 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 position information of a preset marker and a vehicle in real time, wherein the intensity of the second preset range is smaller than that of 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 affecting the operation of the unmanned logistics vehicle due to the intensity of the signal intensity; the marker is a facility for distinguishing driving modes of the region, such as a guideboard, a logo building, a tree, and the like.
Step S40, after detecting that the vehicle reaches the position of the preset marker, acquiring vehicle running information;
step S50, switching working modes according to the running information and the position information of the marker;
In this embodiment, the vehicle travel information includes a vehicle travel route and a vehicle travel speed; judging whether the residual running process is in a complex terrain area or a simple terrain area according to the running route after the vehicle reaches the position of the preset marker, and switching the working mode into a cloud control intelligent driving area when the area where the residual running process is located is in the complex terrain area; when the area where the rest driving process is located belongs to a simple terrain area, switching the working mode into a bicycle intelligent driving area; or in combination with the driving area described in the above embodiment, when the remaining driving process is in the cloud control intelligent driving area, the control operation mode is switched to the cloud control intelligent driving mode, and when the remaining driving process is in the single vehicle intelligent driving area, the operation mode is switched to the single vehicle intelligent driving mode. In addition, in this embodiment, when the vehicle reaches the position range where the preset identifier is located, the vehicle running information may also be obtained.
In the invention, when the signal intensity is in the second preset intensity range, the position information of the preset marker and the vehicle is monitored in real time, and after the position of the preset marker is detected, 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 condition of weak navigation signals is realized, the switching of the driving mode can be accurately carried out by setting the marker, and the problem of conflict between the intelligent driving mode of a bicycle and the intelligent driving mode of cloud control under the condition of weak navigation signals is solved.
Further, referring to fig. 5, a fifth embodiment of the present invention provides an unmanned logistics vehicle control method, based on the embodiment shown in fig. 2, after the step of obtaining 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 in a third preset intensity range, controlling road side equipment to acquire current pose information of a vehicle, a current working mode of the vehicle and 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 strength is within the third preset strength range, the unmanned logistics vehicle cannot acquire the vehicle position and the preset coordinate point, and then the control road side device acquires the current pose information of the vehicle, where the pose information of the vehicle is the position information and the pose information of the vehicle, and it is to be noted that the road side device includes a camera, a laser radar and a road side edge computing 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 one embodiment, the step S70 includes:
step A71, according to the speed of the vehicle, the acceleration of the vehicle, the position of the vehicle, the surrounding environment of the vehicle and the distance between the vehicle and the obstacle. Judging whether the vehicle is at risk of collision 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 surrounding environment images of the vehicle and the distance between the vehicle and an obstacle;
in this embodiment, whether there is a risk of collision between the vehicle and the obstacle may be determined according to the speed, acceleration, position, and obstacle detected during the running of the vehicle, where the obstacle includes an object that impedes the movement of the vehicle, such as a person, a building, a vehicle, an animal, etc., and the present invention is not limited herein.
Step A72, if the vehicle has 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 indicated that human intervention is required to prevent the vehicle from collision when the vehicle has a collision risk, and then the intelligent driving mode of the bicycle needs to be switched to the intelligent driving mode of the cloud control.
Step A73, if the vehicle has no collision risk, switching the current working mode of the vehicle to a single-vehicle intelligent driving mode;
in this embodiment, under the condition that the vehicle has no collision risk, the current working mode of the vehicle can be switched to the intelligent driving mode of the bicycle because no human intervention is required.
According to the invention, whether the vehicle has collision risk or not is judged according to the speed of the vehicle, the acceleration of the vehicle, the position of the vehicle, the surrounding environment image of the vehicle and the distance between the vehicle and an obstacle, and when the vehicle has collision risk and the current working mode is a single-vehicle intelligent driving mode, the single-vehicle intelligent driving mode is switched to a cloud control intelligent driving mode; if the vehicle has no collision risk, the current working mode of the vehicle is switched to the intelligent driving mode of the single vehicle, so that the safety of the vehicle in the driving process can be improved, and collision accidents are avoided.
Further, a sixth embodiment of the present invention provides an unmanned logistics vehicle control method, based on the embodiment shown in fig. 2, the unmanned logistics vehicle control method includes
Step a, when the vehicle working mode is in a cloud control intelligent driving mode, a driving control instruction sent by a terminal device is received;
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 or a tablet, and specifically may send a driving control instruction 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, for example, a control instruction of parking, forwarding, 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 intelligent driving mode of the bicycle, the driving control instruction can be received to switch modes.
In the invention, when the vehicle working mode is in the cloud control intelligent driving mode, a driving control instruction sent by the terminal equipment is received, and the vehicle is controlled to run and the current driving mode is switched according to the driving control instruction, so that the initiative of switching the vehicle mode can be ensured, the vehicle working mode can be switched autonomously, and the safety of the vehicle in the running process is improved.
In addition, the embodiment of the invention also provides a readable storage medium, wherein the readable storage medium is stored with an unmanned logistics vehicle control program, and the unmanned logistics vehicle control program realizes the following operations when being executed by a processor:
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 mutual relation between the vehicle position and the driving area.
Further, when the unmanned logistics vehicle control program is executed by the processor, the following operations are further realized:
the step of acquiring vehicle position information and driving area information and judging the interrelation 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 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;
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, judging 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, judging that the vehicle position is in the single vehicle intelligent driving area.
Further, when the unmanned logistics vehicle control program is executed by the processor, the following operations are further realized:
before the step of obtaining the preset coordinate points of the vehicle position and 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 in the first preset intensity range, executing the step of acquiring the preset coordinate points of the vehicle position and the driving area.
Further, when the unmanned logistics vehicle control program is executed by the processor, the following operations are further realized:
after the step of obtaining the global navigation satellite signal and judging the signal strength of the global navigation satellite signal, the method further comprises the following steps:
if the signal intensity is in a second preset intensity range, monitoring the position information of the preset marker and the 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, acquiring vehicle running information;
and switching the working modes according to the running information and the position information of the marker.
Further, when the unmanned logistics vehicle control program is executed by the processor, the following operations are further realized:
After the step of obtaining the global navigation satellite signal and judging the signal strength of the global navigation satellite signal, the method further comprises the following steps:
when the signal intensity is in a third preset intensity range, controlling road side equipment to acquire current pose information of the vehicle, a current working mode of the vehicle and 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, when the unmanned logistics vehicle control program is executed by the processor, the following operations are further realized:
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 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 an 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 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-vehicle intelligent driving mode.
Further, when the unmanned logistics vehicle control program is executed by the processor, the following operations are further realized: the unmanned logistics vehicle control method comprises the following steps of
When the vehicle working mode is in the cloud control intelligent driving mode, a driving control instruction sent by a terminal device is received;
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 will not be described herein.
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 one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims (5)

1. The unmanned logistics vehicle control method 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;
switching the working mode of the unmanned logistics vehicle according to the mutual relation between the vehicle position and the driving area;
the step of acquiring vehicle position information and driving area information and judging the interrelation 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 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;
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, judging that the vehicle position is in the cloud control intelligent driving area in the driving area;
if the real-time coordinate position is not in the cloud control intelligent driving area, judging that the vehicle position is in the single vehicle intelligent driving area;
Before the step of obtaining the preset coordinate points of the vehicle position and 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;
if the signal intensity is in the first preset intensity range, executing the step of acquiring the preset coordinate points of the vehicle position and the driving area;
the step of acquiring the global navigation satellite signal and judging the signal strength of the global navigation satellite signal further comprises the following steps:
if the signal intensity is in a second preset intensity range, monitoring the position information of the preset marker and the 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, acquiring vehicle running information;
switching working modes according to the running information and the position information of the marker;
the step of acquiring the global navigation satellite signal and judging the signal strength of the global navigation satellite signal further comprises the following steps:
when the signal intensity is in a third preset intensity range, controlling road side equipment to acquire current pose information of the vehicle, a current working mode of the vehicle and surrounding environment of the vehicle, wherein the third preset intensity range is smaller than the second preset intensity range;
Switching the current working mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle;
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 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 an 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 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-vehicle intelligent driving mode.
2. The unmanned logistics vehicle control method of claim 1, wherein the unmanned logistics vehicle control method comprises:
when the vehicle working mode is in the cloud control intelligent driving mode, a driving control instruction sent by a terminal device is received;
And controlling the vehicle to run and switching the current driving mode according to the driving control instruction.
3. An unmanned logistics vehicle control device, characterized by 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 road test equipment comprises a camera, a laser radar and a road test edge calculation unit;
the unmanned logistics vehicle comprises a 5G communication module, a bicycle intelligent driving controller module and a cloud control intelligent driving controller module, wherein the bicycle intelligent driving controller module and the cloud control intelligent driving controller module are connected through a CAN network;
the unmanned logistics vehicle control device is used for acquiring vehicle position information and driving area information, 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;
wherein, unmanned commodity circulation car controlling means specifically is used for:
acquiring a global navigation satellite signal and judging the signal intensity of the global navigation satellite signal;
If the signal intensity is in the first preset intensity range, executing the step of acquiring the preset coordinate points of the vehicle position and the driving area;
if the signal intensity is in a second preset intensity range, monitoring the position information of the preset marker and the 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, acquiring vehicle running information; switching working modes according to the running information and the position information of the marker;
when the signal intensity is in a third preset intensity range, controlling road side equipment to acquire current pose information of the vehicle, a current working mode of the vehicle and surrounding environment of the vehicle, wherein the third preset intensity range is smaller than the second preset intensity range; switching the current working mode of the vehicle according to the current pose information of the vehicle and the surrounding environment of the vehicle;
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;
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, judging that the vehicle position is in the cloud control intelligent driving area in the driving area;
if the real-time coordinate position is not in the cloud control intelligent driving area, judging that the vehicle position is in the single vehicle intelligent driving area;
judging whether the vehicle is at risk of collision 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 an 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 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-vehicle intelligent driving mode.
4. An unmanned logistics vehicle control system, comprising the unmanned logistics vehicle control method and the unmanned logistics vehicle control device, a memory, a processor and an unmanned logistics vehicle control program stored on the memory and capable of running on the processor, wherein the unmanned logistics vehicle control program realizes the steps of the unmanned logistics vehicle control method as set forth in any one of claims 1 to 2 when being executed by the processor.
5. A readable storage medium, wherein an unmanned logistics vehicle control program is stored on the readable storage medium, and when executed by a processor, the unmanned logistics vehicle control program implements the steps of the unmanned logistics vehicle control method as set forth in any one of claims 1 to 2.
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