CN113851003A - Vehicle control system, vehicle control method, vehicle control apparatus, and storage medium - Google Patents
Vehicle control system, vehicle control method, vehicle control apparatus, and storage medium Download PDFInfo
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Abstract
The invention discloses a vehicle control system, a vehicle control method, a vehicle control device and a storage medium, wherein the vehicle control system comprises: the system comprises road side sensing equipment, an unmanned logistics vehicle and a cloud control platform, wherein the road side sensing equipment and the unmanned logistics vehicle are in communication connection with each other; the roadside sensing equipment is arranged at the roadside and comprises a laser radar, a camera and an edge computing unit in communication connection with the laser radar and the camera; the unmanned logistics vehicle comprises a positioning module, an unmanned control module and a vehicle chassis control module which are sequentially connected; the cloud control platform comprises a global optimization module and a task scheduling module which are connected with each other. The invention improves the use experience of unmanned logistics.
Description
Technical Field
The present invention relates to the field of vehicles, and in particular, to a vehicle control system, a vehicle control method, a vehicle control device, and a storage medium.
Background
With the continuous development of the unmanned driving technology and the 5G communication technology and the iteration of products, the unmanned logistics are applied more and more in specific scenes such as closed roads and semi-closed roads. The logistics transportation field is one of the important application scenes of unmanned vehicles, and attracts wide attention in the academic world and the industrial world. The unmanned vehicle is adopted to carry out logistics, transportation and distribution of goods, so that the cost of human resources can be greatly reduced, the labor productivity is liberated, and the intelligent level of human life is improved.
In the current stage, unmanned logistics based on a single-vehicle intelligent scheme has the problems of limited vehicle sensing range, low multi-vehicle collaborative transportation efficiency, high single-vehicle cost and the like, and the degree of intelligence of the unmanned logistics is low.
Disclosure of Invention
The invention mainly aims to provide a vehicle control system, a vehicle control method, vehicle control equipment and a storage medium, and aims to solve the problem of low intelligent degree of the existing unmanned logistics.
To achieve the above object, the present invention provides a vehicle control system comprising:
the system comprises road side sensing equipment, an unmanned logistics vehicle and a cloud control platform, wherein the road side sensing equipment and the unmanned logistics vehicle are in communication connection with each other;
the roadside sensing equipment is installed at the roadside and comprises a laser radar, a camera and an edge computing unit in communication connection with the laser radar and the camera;
the unmanned logistics vehicle comprises a positioning module, an unmanned control module and a vehicle chassis control module which are sequentially connected;
the cloud control platform comprises a global optimization module and a task scheduling module which are connected with each other.
In order to achieve the above object, the present invention also provides a vehicle control method including the steps of:
acquiring perception information in a preset area and driving information of an unmanned logistics vehicle, and generating a global driving path according to the perception information in the preset area and the driving information;
and controlling the unmanned logistics vehicle to run according to the global running path, and optimizing the running path according to the running information.
Optionally, the step of acquiring perception information in a preset area and driving information of the unmanned logistics vehicle, and generating a global driving path according to the perception information in the preset area and the driving information includes:
acquiring perception information in a preset range, wherein the perception information comprises information of traffic participants in a preset working range of the unmanned logistics vehicle, and
acquiring running information within a preset range, wherein the running information comprises real-time position information, vehicle speed and course angle information of the unmanned logistics vehicle within a preset working range of the unmanned logistics vehicle;
and generating a global driving path according to the perception information, the driving information and a preset destination.
Optionally, the step of acquiring perception information including information of traffic participants in a preset working range of the unmanned logistics vehicle includes:
acquiring a traffic participant target in a preset working range of the unmanned logistics vehicle;
acquiring and filtering repeated traffic participant targets in the preset working range according to the traffic participant targets in the preset working range;
and generating perception information according to the filtered traffic participant target.
Optionally, the step of controlling the unmanned logistics vehicle to travel according to the global travel path and optimizing the travel path according to the travel information includes:
controlling the unmanned logistics vehicle to run according to the global running path, and judging whether an obstacle exists in a first preset range in real time according to the sensing information and the running information;
if the first preset range has the obstacle, judging whether the obstacle is an obstacle vehicle or not according to the perception information;
if the obstacle is an obstacle vehicle, acquiring the motion state and the running speed of the obstacle vehicle;
and if the motion state of the obstacle vehicle is driving, controlling the unmanned logistics vehicle to follow the obstacle vehicle at the same driving speed according to the driving information.
Optionally, if the motion state of the obstacle vehicle is driving, the step of controlling the unmanned logistics vehicle to follow the obstacle vehicle at the same driving speed according to the driving information includes:
if the motion state of the obstacle vehicle is driving, acquiring the real-time driving direction of the obstacle vehicle, and judging whether the real-time driving direction of the obstacle vehicle is the same as the driving direction of the global driving path or not;
and if the real-time running direction of the obstacle vehicle is the same as the running direction of the global running path, obtaining the running information of the unmanned logistics vehicle, and controlling the unmanned logistics vehicle to run with the obstacle vehicle at the same running speed according to the running information.
Optionally, after the step of determining whether the obstacle is a vehicle according to the perception information, the method further includes:
if the obstacle is not a vehicle, acquiring the running speed of the obstacle according to the perception information;
and optimizing a global driving path according to the driving speed of the barrier and the perception information, and controlling the unmanned logistics vehicle to drive according to the optimized group-living driving path.
Optionally, after the step of controlling the unmanned logistics vehicle to travel according to the global travel path and optimizing the travel path according to the travel information, the method further includes:
and receiving a vehicle control instruction sent by the terminal equipment, and controlling the unmanned logistics vehicle to execute corresponding operation according to the control instruction.
To achieve the above object, the present invention also provides a vehicle control apparatus including a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the vehicle control method as described above.
To achieve the above object, the present invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the vehicle control method as described above.
According to the vehicle control system, the vehicle control method, the vehicle control equipment and the storage medium, the unmanned logistics vehicle and the sensing equipment are separately arranged through the roadside sensing equipment and the unmanned logistics vehicle which are in communication connection with each other and the cloud control platform which is in communication connection with the unmanned logistics vehicle and the roadside sensing equipment, the roadside sensing equipment is arranged on the roadside, the information acquisition range of the unmanned logistics vehicle is expanded, and the manufacturing cost of the unmanned logistics vehicle can be reduced; through the laser radar, the camera and the edge calculation unit in communication connection with the laser radar and the camera, the roadside sensing equipment can fully acquire peripheral sensing information so as to enable the unmanned logistics vehicle to run and work more accurately; the unmanned logistics vehicle can be controlled to work through the global optimization module and the task scheduling module in the cloud control platform; through the unmanned logistics vehicle, the positioning module, the unmanned control module and the vehicle chassis control module which are sequentially connected, the control command sent by the cloud control platform can be received and corresponding operation is executed to control the unmanned logistics vehicle to work, so that the human resource cost is reduced, the labor productivity is liberated, and the intelligent level of life is improved.
Drawings
FIG. 1 is a schematic diagram of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a vehicle control system according to the present invention;
FIG. 3 is a flowchart illustrating a first embodiment of a vehicle control method according to the present invention;
FIG. 4 is a flowchart illustrating a second embodiment of a vehicle control method according to the present invention;
FIG. 5 is a flowchart illustrating a third exemplary embodiment of a vehicle control method according to the present invention;
FIG. 6 is a flowchart illustrating a fourth embodiment of a vehicle control method according to the present invention;
fig. 7 is a flowchart illustrating a fifth embodiment of the vehicle control method according to the present 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
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of a hardware structure of a vehicle control device provided in each embodiment of the present invention. The vehicle control apparatus includes components such as a communication module 01, a memory 02, and a processor 03. Those skilled in the art will appreciate that the vehicle control apparatus shown in fig. 1 may also include more or fewer components than shown, or combine certain components, or a different arrangement of components. The processor 03 is connected to the memory 02 and the communication module 01, respectively, and the memory 02 stores a computer program, which is executed by the processor 03 at the same time.
The communication module 01 may be connected to an external device through a network. The communication module 01 may receive data sent by an external device, and may also send data, instructions, and information to the external device, where the external device may be an electronic device such as a mobile phone, a tablet computer, a notebook computer, and a desktop computer.
The memory 02 may be used to store software programs and various data. The memory 02 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data or information created according to the use of the vehicle control apparatus, or the like. Further, the memory 02 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 03, which is a control center of the vehicle control apparatus, connects various parts of the entire vehicle control apparatus using various interfaces and lines, and performs various functions of the vehicle control apparatus and processes data by operating or executing software programs and/or modules stored in the memory 02 and calling data stored in the memory 02, thereby performing overall monitoring of the vehicle control apparatus. Processor 03 may include one or more processing units; preferably, the processor 03 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 03.
Referring to fig. 2, in an embodiment, the vehicle control apparatus further includes:
the system comprises roadside sensing equipment 003 and an unmanned logistics vehicle 002 which are in communication connection with each other, and a cloud control platform 001 which is in communication connection with the unmanned logistics vehicle 002 and the roadside sensing equipment 003; the roadside sensing device 003 is installed at the roadside and comprises a laser radar 010 and a camera 011, and an edge calculation unit 009 in communication connection with the laser radar 010 and the camera 011; the unmanned logistics vehicle 002 comprises a positioning module 006, an unmanned control module 007 and a vehicle chassis control module 008 which are connected in sequence; the cloud control platform 001 comprises a global optimization module 004 and a task scheduling module 005 which are connected with each other.
In this embodiment, the roadside sensing equipment 003 may be integrally installed on a roadside street lamp, or the roadside sensing equipment 003 may be directly installed on both sides of the road, where the number of the laser radar 010 and the cameras 011 may be 1 or more, in this embodiment, the laser radar 010 and the cameras 011 are 2, and the laser radar 010 and the cameras 011 may be installed on the upper portion of a lamp post and the bottom portion of the lamp post, so as to achieve acquisition of perception information on a road surface and send the acquired perception information to the edge computing unit 009, and at the same time, the laser radar 010 and the cameras 011 are installed on the roadside, which may expand an acquisition range of perception information to achieve global path planning of the unmanned logistics vehicle 002 and also reduce manufacturing cost of the unmanned logistics vehicle 002; the edge calculation unit 009 is configured to receive the perception information, exclude repeated and false traffic participant targets in the perception information according to the perception information, and send the filtered traffic participant targets to the cloud control platform 001, where it should be noted that the perception information includes the traffic participant targets, and the traffic participant targets include people, vehicles, other obstacles, and the like in a road range; the positioning module 006 in the unmanned logistics vehicle 002 is specifically a GPS positioning module 006, the GPS positioning module 006 is configured to acquire actual position information of the unmanned logistics vehicle 002 and send the actual position information to the cloud control platform 001 through 5G, the unmanned control module 007 is configured to control the vehicle chassis control module 008 according to travel path information, and the vehicle chassis control module 008 is configured to control actual travel of the unmanned logistics vehicle 002; the cloud control platform 001 is specifically a 5G cloud control platform 001, and the global optimization module 004 is configured to perform real-time global driving path optimization on the unmanned logistics vehicle 002 according to the perception information, output the global driving path information, and send the global driving path information to the task scheduling module 005; the task scheduling module 005 is configured to issue the global travel path information to the unmanned logistics vehicle 002 through 5G. In this embodiment, all data transmission and reception and information transmission are realized by the 5G technology.
According to the vehicle control device and the vehicle control method, the unmanned logistics vehicle and the sensing device are separately arranged through the roadside sensing device and the unmanned logistics vehicle which are in communication connection with each other and the cloud control platform which is in communication connection with the unmanned logistics vehicle and the roadside sensing device, the roadside sensing device is arranged on the roadside, the information acquisition range of the unmanned logistics vehicle is expanded, the global path planning of the logistics vehicle in the working process is met, the unmanned logistics vehicle can change a driving path in real time or in advance according to the change of the sensing information, and the manufacturing cost of the unmanned logistics vehicle can be reduced; through the laser radar, the camera and the edge calculation unit in communication connection with the laser radar and the camera, the roadside sensing equipment can fully acquire peripheral sensing information so as to enable the unmanned logistics vehicle to run and work more accurately; the unmanned logistics vehicle can be controlled to work through the global optimization module and the task scheduling module in the cloud control platform; through the unmanned logistics vehicle, the positioning module, the unmanned control module and the vehicle chassis control module which are sequentially connected, the control command sent by the cloud control platform can be received and corresponding operation is executed to control the unmanned logistics vehicle to work, so that the human resource cost is reduced, the labor productivity is liberated, and the intelligent level of life is improved.
Those skilled in the art will appreciate that the configuration of the unmanned logistics vehicle illustrated in fig. 1 is not intended to be limiting and can include more or fewer components than illustrated, or some components in combination, or a different arrangement of components.
Various embodiments of the method of the present invention are presented in terms of the above-described hardware architecture.
Referring to fig. 3, in a first embodiment of the vehicle control method of the invention, the vehicle control method includes the steps of:
step S10, acquiring perception information in a preset area and driving information of the unmanned logistics vehicle, and generating a global driving path according to the perception information in the preset area and the driving information;
in this embodiment, the preset area is specifically a working range of the unmanned logistics vehicle, the sensing information of the preset area is acquired through roadside sensing devices installed on two sides of a road, the number of the roadside sensing devices is multiple, and the sensing ranges are overlapped between two adjacent sensing devices; the preset area can also be all areas where roadside sensing devices are installed, that is, the range which can be sensed by all roadside sensing devices is the preset area. The perception information comprises traffic participant targets and traffic participant target information, and specifically, the traffic participant targets comprise people, vehicles and other obstacles in a preset area; the traffic participant information includes the type, location, size, speed, etc. of the traffic participant objective. The driving information comprises positioning information, vehicle speed information, course angle and the like of the unmanned logistics vehicle; the global travel path is an optimal travel path of the unmanned logistics vehicle during work, such as an optimal path between the unmanned logistics vehicle and a destination during cargo delivery. The global running path can be a path which needs to turn the least in the running process of the logistics vehicle, can also be a path with the least number of traffic participant targets, can also be a path with the shortest running path, and can also be a path which needs to pass through the least traffic lights.
And step S20, controlling the unmanned logistics vehicle to run according to the global running path, and optimizing the running path according to the running information.
In this embodiment, the cloud control module can control the unmanned logistics vehicles to travel on the global travel path to complete logistics distribution work, and when the unmanned logistics vehicles encounter an unexpected situation, the travel path can be automatically adjusted or changed according to the travel information, wherein the unexpected situation includes the encounter with other unmanned logistics vehicles or other motor vehicles, the encounter with obstacles during the travel process, and the like.
In the invention, perception information in a preset area and driving information of an unmanned logistics vehicle are obtained, and a global driving path is generated according to the perception information in the preset area and the driving information; the unmanned logistics vehicle is controlled to run according to the global running path, running path optimization is carried out according to the running information, global running path planning of the vehicle control equipment during working can be achieved, logistics distribution work can be completed on the optimal running path through the unmanned logistics vehicle, manpower is saved, logistics distribution intelligence is improved, meanwhile, the running path can be optimized in real time, and the intelligence of the unmanned logistics vehicle is improved.
Further, referring to fig. 4, in the vehicle control method according to the present invention proposed based on the first embodiment of the present invention, which proposes the second embodiment, the step S10 includes:
step S101, obtaining perception information in a preset range, wherein the perception information comprises information of traffic participants in a preset working range of the unmanned logistics vehicle and the information of the traffic participants
Acquiring running information within a preset range, wherein the running information comprises real-time position information, vehicle speed and course angle information of the unmanned logistics vehicle within a preset working range of the unmanned logistics vehicle;
in this embodiment, a traffic participant target within a preset working range needs to be acquired through the roadside sensing device, and then traffic participant information of a specific traffic participant target is acquired according to the traffic participant target, where the traffic participant target includes people, cars and other obstacles within the preset working range; the traffic participant information comprises the type, position, size, speed and the like of a traffic participant target, and specifically, the perception information can be acquired through the laser radar and the camera; the running information can be reported in real time through the unmanned logistics vehicle, the running information comprises positioning information, vehicle speed information, course angle information and the like of the unmanned logistics vehicle, specifically, the positioning information can be obtained through a positioning module of the unmanned logistics vehicle, and the course angle information can be obtained through an inertial sensor.
And S102, generating a global driving path according to the perception information, the driving information and a preset destination.
In this embodiment, the preset destination is a distribution destination of the unmanned logistics vehicle, the number of the preset destinations may be 1 or more, and when the number of the preset destinations is 1, an optimal driving path from the distribution starting point to the preset destination of the unmanned logistics vehicle is directly generated according to the perception information and the driving information; when the preset destination is multiple, according to the distance between the preset destination and the distribution starting point, the global driving paths are sequentially generated from near to far, the global driving paths can also be sequentially generated from few to many turns as required, the global driving paths can also be sequentially generated from few to many of the target number of the traffic participants, the global driving paths can also be sequentially generated from short to long of the driving paths, or the global driving paths can also be sequentially generated from few to many of the driving paths through traffic lights, and a person skilled in the art can automatically set how to generate the global paths according to the requirement so as to realize the optimal global path setting of the unmanned logistics vehicle.
In the invention, the work efficiency of the unmanned logistics vehicle can be improved by acquiring the perception information comprising the information of the traffic participants in the preset work range of the unmanned logistics vehicle, acquiring the driving information comprising the real-time position information, the vehicle speed and the course angle information of the unmanned logistics vehicle, and generating the global driving path according to the driving information and the preset destination, and the energy consumption of the unmanned logistics vehicle can be saved and the work time can be prolonged by generating the optimal path.
Further, referring to fig. 5, in the vehicle control method according to the present invention proposed based on the first embodiment of the present invention, the present invention proposes a third embodiment, and the step of acquiring perception information including traffic participant information within a first preset range of the unmanned logistics vehicle includes:
step S1011, obtaining a traffic participant target in a preset working range of the unmanned logistics vehicle;
in this embodiment, because a plurality of roadside sensing devices exist in the preset working range, each sensing device has the same sensing range, and adjacent sensing ranges overlap, it is necessary to acquire a traffic participant target sensed by each roadside sensing device in the preset working range, and then filter all traffic participant targets. Specifically, a traffic participant target in a range corresponding to the roadside sensing device can be obtained through a combination of a camera and a laser radar in the roadside sensing device;
step S1012, according to the traffic participant targets in the preset working range, acquiring and filtering repeated traffic participant targets in the preset working range;
in this embodiment, after the traffic participant targets in the preset working range are obtained, filtering needs to be performed according to the traffic participant targets in the preset working range obtained by the different roadside sensing devices, repeated traffic participant targets may be identified through a preset image identification algorithm, and the filtering process is implemented through the edge calculation unit. The traffic participant target and the traffic participant information are explained in the above embodiments, and are not described herein again.
Step S1013, generating perception information according to the filtered traffic participant target;
in this embodiment, repeated traffic participant targets are filtered, and then the perception information is generated according to the remaining traffic participant targets, wherein the perception information includes traffic participant targets and traffic participant target information, and specifically, the traffic participant targets include people, cars and other obstacles in a preset area; the traffic participant information includes the type, location, size, speed, etc. of the traffic participant objective.
In the invention, a traffic participant target in a preset working range of the unmanned logistics vehicle is obtained; acquiring and filtering repeated traffic participant targets in the preset working range according to the traffic participant targets in the preset working range; and generating perception information according to the filtered traffic participant target, thereby avoiding repeated acquisition of the traffic participant target, thereby generating a wrong global driving path and improving the accuracy of the perception information.
Further, referring to fig. 6, in the vehicle control method according to the invention proposed based on the first embodiment of the invention, which proposes the fourth embodiment, the step S20 includes:
step S21, controlling the unmanned logistics vehicle to run according to the global running path, and judging whether an obstacle exists in a first preset range in real time according to the perception information and the running information;
in this embodiment, the first preset range specifically refers to a range of the unmanned logistics vehicle within 100 meters before and after the unmanned logistics vehicle is within a road range, with the unmanned logistics vehicle in driving as a center in an actual driving process, and of course, a person skilled in the art may select the range within 125 meters before and after, 150 meters before and after, and 175 meters before and after, which is not limited herein; the sensing information in the first preset range can be obtained by obtaining real-time position information of the unmanned logistics vehicles and adopting roadside sensing equipment located in the first preset range of the unmanned logistics vehicles according to the real-time position information, and the driving information is obtained by real-time reporting of the unmanned logistics vehicles.
Step S22, if an obstacle exists in the first preset range, judging whether the obstacle is an obstacle vehicle or not according to the perception information;
in this embodiment, the obstacle is an object different from the traffic participant target, the obstacle may be obtained in real time according to the roadside sensing device, and the type of the obstacle includes a movable object such as a vehicle or a person.
Step S23, if the obstacle is an obstacle vehicle, acquiring the motion state and the running speed of the obstacle vehicle;
the motion state includes a stationary state and a traveling state.
Step S24, if the motion state of the obstacle vehicle is driving, controlling the unmanned logistics vehicle to follow the obstacle vehicle at the same driving speed according to the driving information;
in this embodiment, it is further required to determine whether the vehicle is another unmanned logistics vehicle controlled by the cloud control platform, and if the vehicle is another unmanned logistics vehicle controlled by the cloud control platform, the driving route is re-planned according to the task scheduling module in the cloud control platform.
In one embodiment, step S24 further includes:
step a, if the motion state of the obstacle vehicle is driving, acquiring the real-time driving direction of the obstacle vehicle, and judging whether the real-time driving direction of the obstacle vehicle is the same as the driving direction of the global driving path;
in this embodiment, the obstacle vehicle is another motor vehicle controlled by a non-cloud-control platform, and the real-time driving direction of the obstacle vehicle may be obtained according to a roadside sensing device, and whether the real-time driving direction of the obstacle vehicle is the same as the current driving direction of the global driving path is determined;
and b, if the real-time running direction of the obstacle vehicle is the same as the running direction of the global running path, obtaining the running information of the unmanned logistics vehicle, and controlling the unmanned logistics vehicle to run with the obstacle vehicle at the same running speed according to the running information.
In this embodiment, the driving speed of the obstacle vehicle may be obtained through the roadside sensing device, specifically, the time of the obstacle vehicle in a certain distance may be obtained, the speed of the obstacle vehicle may be obtained according to the distance and the time, and the speed of the obstacle vehicle may also be directly identified according to a preset algorithm; after the speed of the barrier vehicle is obtained, the current speed of the unmanned logistics vehicle is adjusted according to the running information of the unmanned logistics vehicle to be consistent with the speed of the barrier vehicle, and the unmanned logistics vehicle runs with the barrier vehicle, so that the safety of the unmanned logistics vehicle in the distribution process is guaranteed.
In the invention, by controlling the unmanned logistics vehicle to run according to the global running path and judging whether an obstacle exists in a first preset range in real time according to the sensing information and the running information, the identification of the obstacle which does not exist in the global running path in the running process of the unmanned logistics vehicle can be identified; whether the barrier is a barrier vehicle is judged according to the sensing information, the motion state and the running speed of the barrier vehicle are obtained, the unmanned logistics vehicle is controlled to run with the barrier vehicle at the same running speed according to the running information, the safety of the unmanned logistics vehicle in the working process is fully guaranteed, the unmanned logistics vehicle is prevented from overtaking a road with a motor vehicle, and the running path is optimized.
Further, referring to fig. 7, in the vehicle control method according to the present invention proposed in the first embodiment of the present invention, the present invention proposes a fifth embodiment, and after step S22, the method further includes:
step S221, if the obstacle is not a vehicle, acquiring the running speed of the obstacle according to the perception information;
in this embodiment, the obstacle may also be a walking person, animal or other movable object;
and step S222, optimizing a global driving path according to the driving speed of the obstacle and the perception information, and controlling the unmanned logistics vehicle to drive according to the optimized community driving path.
In this embodiment, the optimizing the global driving path may be adjusting a change speed of the unmanned logistics vehicle according to a driving speed of the obstacle to avoid the obstacle and continue driving; or the local driving path in the all-in-one driving path is changed to achieve the purpose of avoiding the barrier; the process is carried out through a global optimization module in the cloud control platform, the optimized global driving path is sent to a task scheduling module, and meanwhile, the optimized global driving path is sent to the unmanned logistics vehicle through the task scheduling module, so that the unmanned logistics vehicle is controlled to change the driving path.
According to the method and the system, the running speed of the barrier is obtained according to the sensing information, the global running path is optimized according to the running speed of the barrier and the sensing information, and the unmanned logistics vehicle is controlled to run according to the optimized group-living running path, so that the unmanned logistics vehicle can change the running path according to an accident situation, manual operation is avoided, and the working intelligence degree of the unmanned logistics vehicle is improved.
Further, in the vehicle control method according to the present invention proposed in the first embodiment of the present invention, the present invention proposes a sixth embodiment, and after step S20, the method further includes:
and receiving a vehicle control instruction sent by the terminal equipment, and controlling the unmanned logistics vehicle to execute corresponding operation according to the control instruction.
In this embodiment, the terminal device can be intelligent products such as cell-phone, panel, computer, and the user can send control command to the cloud accuse platform according to terminal device to make the cloud accuse platform control unmanned commodity circulation car according to control command and carry out corresponding operation, control command includes but not limited to the emergency brake of unmanned commodity circulation car, accelerates, slows down, turns to, far and near light, indicator and emergency lamp control.
In the invention, the unmanned logistics vehicle is controlled to execute corresponding operation according to the control instruction by receiving the vehicle control instruction sent by the terminal equipment, so that the unmanned logistics vehicle is manually controlled, the situation that the unmanned logistics vehicle cannot make corresponding operation in time in emergency is avoided, and the safety of the unmanned logistics vehicle in working is improved.
The invention also proposes a storage medium on which a computer program is stored. The storage medium may be the Memory 02 in the unmanned logistics vehicle of fig. 1, and may also be at least one of a ROM (Read-Only Memory)/RAM (Random Access Memory), a magnetic disk, and an optical disk, where the storage medium includes several pieces of information for enabling the unmanned logistics vehicle to perform the method according to the embodiments of the present invention.
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.
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 vehicle control system, characterized by comprising:
the system comprises road side sensing equipment, an unmanned logistics vehicle and a cloud control platform, wherein the road side sensing equipment and the unmanned logistics vehicle are in communication connection with each other;
the roadside sensing equipment is arranged at the roadside and comprises a laser radar, a camera and an edge computing unit in communication connection with the laser radar and the camera;
the unmanned logistics vehicle comprises a positioning module, an unmanned control module and a vehicle chassis control module which are sequentially connected;
the cloud control platform comprises a global optimization module and a task scheduling module which are connected with each other.
2. A vehicle control method characterized by comprising the steps of:
acquiring perception information in a preset area and driving information of an unmanned logistics vehicle, and generating a global driving path according to the perception information in the preset area and the driving information;
and controlling the unmanned logistics vehicle to run according to the global running path, and optimizing the running path according to the running information.
3. The vehicle control method according to claim 2, wherein the step of acquiring the perception information in a preset area and the travel information of the unmanned logistics vehicle, and generating the global travel path according to the perception information in the preset area and the travel information comprises:
acquiring perception information in a preset range, wherein the perception information comprises information of traffic participants in a preset working range of the unmanned logistics vehicle, and
acquiring running information within a preset range, wherein the running information comprises real-time position information, vehicle speed and course angle information of the unmanned logistics vehicle within a preset working range of the unmanned logistics vehicle;
and generating a global driving path according to the perception information, the driving information and a preset destination.
4. The vehicle control method according to claim 3, wherein the step of acquiring perception information including traffic participant information within a preset working range of the unmanned logistics vehicle comprises:
acquiring a traffic participant target in a preset working range of the unmanned logistics vehicle;
acquiring and filtering repeated traffic participant targets in the preset working range according to the traffic participant targets in the preset working range;
and generating perception information according to the filtered traffic participant target.
5. The vehicle control method according to claim 1, wherein the step of controlling the unmanned logistics vehicle to travel according to the global travel path and performing travel path optimization according to the travel information includes:
controlling the unmanned logistics vehicle to run according to the global running path, and judging whether an obstacle exists in a first preset range in real time according to the sensing information and the running information;
if the first preset range has the obstacle, judging whether the obstacle is an obstacle vehicle or not according to the perception information;
if the obstacle is an obstacle vehicle, acquiring the motion state and the running speed of the obstacle vehicle;
and if the motion state of the obstacle vehicle is driving, controlling the unmanned logistics vehicle to follow the obstacle vehicle at the same driving speed according to the driving information.
6. The vehicle control method according to claim 5, wherein the step of controlling the unmanned physical distribution vehicle to follow the vehicle at the same traveling speed as the obstacle vehicle based on the traveling information if the moving state of the obstacle vehicle is traveling comprises:
if the motion state of the obstacle vehicle is driving, acquiring the real-time driving direction of the obstacle vehicle, and judging whether the real-time driving direction of the obstacle vehicle is the same as the driving direction of the global driving path or not;
and if the real-time running direction of the obstacle vehicle is the same as the running direction of the global running path, obtaining the running information of the unmanned logistics vehicle, and controlling the unmanned logistics vehicle to run with the obstacle vehicle at the same running speed according to the running information.
7. The vehicle control method according to claim 5, further comprising, after the step of determining whether the obstacle is a vehicle based on the perception information:
if the obstacle is not a vehicle, acquiring the running speed of the obstacle according to the perception information;
and optimizing a global driving path according to the driving speed of the barrier and the perception information, and controlling the unmanned logistics vehicle to drive according to the optimized group-living driving path.
8. The vehicle control method according to claim 2, wherein after the step of controlling the unmanned logistics vehicle to travel according to the global travel path and performing travel path optimization according to the travel information, the method further comprises:
and receiving a vehicle control instruction sent by the terminal equipment, and controlling the unmanned logistics vehicle to execute corresponding operation according to the control instruction.
9. A vehicle control apparatus, characterized in that the vehicle control apparatus comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the vehicle control method according to any one of claims 2 to 8.
10. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the vehicle control method according to any one of claims 2 to 8.
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