CN114200937B - Unmanned control method based on GPS positioning and 5G technology - Google Patents
Unmanned control method based on GPS positioning and 5G technology Download PDFInfo
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- 230000008054 signal transmission Effects 0.000 claims description 8
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- 210000002569 neuron Anatomy 0.000 claims description 3
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- 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 control method based on GPS positioning and 5G technology, which comprises the steps of collecting vehicle running state data through a sensing module arranged on a vehicle; the state data is received through a state acquisition module connected and communicated with the sensing module, and the received state data is classified by a state classification module connected with the state acquisition module, so that the running state data of the vehicle is classified into a road condition state and a vehicle body state; the parameter adjustment module receives the classification data of the state classification module, compares the received road condition state and vehicle body state with corresponding standard parameters, and calculates an error; the calculated error data is transmitted to a driving module, and the driving module adjusts driving parameters according to the error data so as to operate and drive the vehicle to run.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle, in particular to an unmanned control method based on GPS positioning and 5G technology.
Background
Unmanned car is one kind of intelligent car, also called wheeled mobile robot, mainly relies on the intelligent pilot that takes computer system as the main in the car to realize unmanned purpose, in real life, GPS location mainly is used for carrying out a technique of long-range real-time positioning control to mobile people, pet, car and equipment.
GPS positioning is a positioning technology combining a GPS technology, a wireless communication technology, an image processing technology and a GIS technology, and a fifth generation mobile communication technology is a new generation broadband mobile communication technology with the characteristics of high speed, low time delay and large connection, and is a network infrastructure for realizing man-machine object interconnection.
Unmanned among the prior art mainly carries out vehicle prejudgement and parameter control through the mode of camera formation of image, and the precision is not enough, promptly, to the data extraction of road conditions and the ability of planning the route relatively poor, only can snatch vehicle and topography situation around through the mode of making a video recording, cause the data volume of extracting too big, can't come with standard parameter's comparison according to road conditions state and automobile body state, calculate the error, can't adjust driving module's parameter according to the error more, lead to unmanned precision not enough, easily take place dangerous problem when having the error too big.
Disclosure of Invention
The invention aims to provide an unmanned control method based on GPS positioning and 5G technology, so as to solve the problems.
The technical scheme of the invention is as follows:
an unmanned control method based on GPS positioning and 5G technology comprises the following steps:
s1, determining position information of a vehicle body through a GPS positioning module, and acquiring vehicle running state data through a sensing module arranged on the vehicle;
s2, receiving state data through a state acquisition module connected and communicated with the sensing module, classifying the received state data by a state classification module connected with the state acquisition module, and classifying the running state data of the vehicle into a road condition state and a vehicle body state;
s3, receiving classification data of the state classification module according to the parameter adjustment module, and comparing the received road condition state and vehicle body state with corresponding standard parameters to calculate an error;
and S4, transmitting the calculated error data to a driving module through a 5G signal, and adjusting driving parameters by the driving module according to the error data, so as to operate and drive the vehicle to run.
Further, the driving module in S4 is disposed on the vehicle, and is used for driving and operating the vehicle, and parameters of the driving module include: the steering parameters are steering wheel rotation angles and directions; the accelerator parameter is the lifting height of the accelerator; the brake parameter is the lifting height of the brake; and the gear parameter is a gear of the vehicle body driving.
Further, the driving module is communicated with a remote service platform through a remote signal transmission module, the remote service platform is used for customer service to remotely maintain and acquire the condition of the vehicle, and the remote signal transmission module adopts 5G signal remote transmission.
Further, the sensing module in S1 includes: the radar laser module is combined with the GPS positioning module, draws a map through the determined vehicle body position information, and detects surrounding vehicle information; the camera module is used for shooting surrounding road conditions and collecting surrounding road condition images; and the sonar module is used for detecting the distance between the sonar module and other vehicles on the road.
Further, the state classification module in S2 includes: the road condition extraction module is used for extracting vehicle data and road data around the running of the vehicle body, and the vehicle condition extraction module is used for extracting parameters of the vehicle body.
Further, the method further comprises the following steps: the road condition state extraction module and the vehicle body state extraction module are both connected with the parameter adjustment module, the parameter adjustment module adjusts according to errors among standard parameters, extracted road condition parameters and vehicle body parameters, the road condition state extraction module is connected with the CNN recognition module and the path planning module, the CNN recognition module is used for recognizing road conditions and reasonably planning a driving path through the path planning module, and the path planning module is connected with the parameter adjustment module.
Further, the CNN recognition module includes: the convolution layer feature extraction module is used for extracting different features from the input image by using different filters; the activation layer module is connected with the convolution layer feature extraction module and used for controlling the opening of the target neuron; the convergence layer module is connected with the activation layer module and is used for compressing the space occupied by the feature map so as to reduce the number of parameters; and the object tracking module is connected with the convergence layer module and is used for tracking the object according to the predicted object running track after the object is identified.
Further, the parameter adjustment module includes: the system comprises a vehicle body condition extraction module, a real parameter generation module, a parameter comparison module and an error calculation module which are sequentially connected, wherein the real parameter generation module is used for listing parameters obtained by the vehicle body condition extraction module, comparing the real parameters with standard parameters through the parameter comparison module, and calculating errors through the error calculation module.
Further, the parameter adjustment module is connected with an information base module, and the information base module comprises a standard parameter input module which is used for inputting and recording standard parameter data under various road conditions.
Further, the parameter adjustment module further comprises a fault detection module, the fault detection module comprises an error threshold setting module, a threshold comparison module and an alarm module, the error threshold setting module is used for setting an error range between the standard parameter and the real parameter, the threshold comparison module is used for comparing whether the error between the opposite standard parameter and the real parameter is in the threshold range or not, and the alarm module alarms if the error between the opposite standard parameter and the real parameter is not in the threshold range.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention collects the vehicle form state data through the sensing module, then the state data is obtained through the state obtaining module and classified into the road condition state and the vehicle body state by the state classifying module, the road condition state and the vehicle body state are compared with the standard parameters, the error is calculated, and the parameters of the driving module are adjusted according to the error.
2. According to the road condition state extraction module, the data is compressed and processed through the CNN recognition module, the work load of the system is reduced, the system is operated and operated more smoothly, the surrounding vehicles are recognized more accurately, and compared with the traditional method that shooting judgment is carried out only through a camera, the accuracy is higher, and the safety is improved.
3. According to the invention, through the set fault detection module, the threshold range of the error can be flexibly set, and when the parameter cannot be self-aligned by the driving module, an early warning is sent out, so that the driver can be ensured to adjust to the artificial control state in time, and the driving safety is improved.
4. According to the invention, through the remote service platform and the remote signal transmission module, 5G signals are transmitted through the remote signal transmission module, so that the speed is faster, the communication between the remote service platform and a driver is more convenient, the long-term tracking of the driving module is facilitated, the failure of timely discovery when errors occur is prevented, and the later-stage big data collection of the driving module and the timely adjustment of a system are more facilitated.
Drawings
FIG. 1 is a schematic diagram of an unmanned control method of the present invention;
FIG. 2 is a schematic diagram of a CNN recognition module according to the present invention;
FIG. 3 is a schematic diagram of a parameter adjustment module according to the present invention;
FIG. 4 is a schematic diagram of a fault detection module according to the present invention;
FIG. 5 is a schematic diagram of a driving module placement of the unmanned control method of the present invention;
FIG. 6 is a schematic diagram of a sensor module according to the present invention.
Detailed Description
The following describes in detail the embodiments of the present invention with reference to fig. 1 to 6. In the description of the present invention, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second" may include one or more such features, either explicitly or implicitly; in the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
It should be noted that, the circuit connection related in the present invention adopts a conventional circuit connection manner, and no innovation is related.
Examples
As shown in fig. 1 to 6, a method for unmanned control based on GPS positioning and 5G technology includes:
s1, determining position information of a vehicle body through a GPS positioning module, and acquiring vehicle running state data through a sensing module arranged on the vehicle;
s2, receiving state data through a state acquisition module connected and communicated with the sensing module, classifying the received state data by a state classification module connected with the state acquisition module, and classifying the running state data of the vehicle into a road condition state and a vehicle body state;
s3, receiving classification data of the state classification module according to the parameter adjustment module, and comparing the received road condition state and vehicle body state with corresponding standard parameters to calculate an error;
and S4, transmitting the calculated error data to a driving module through a 5G signal, and adjusting driving parameters by the driving module according to the error data, so as to operate and drive the vehicle to run.
Specifically, the driving module in S4 is disposed on the vehicle, and is used for driving and operating the vehicle, where the parameters of the driving module include a steering parameter, an accelerator parameter, a brake parameter and a gear parameter, and the steering parameter is a steering wheel rotation angle and direction; the accelerator parameter is the lifting height of the accelerator; the braking parameter is the lifting height of the brake; the gear parameter is the gear of the vehicle body driving.
Preferably, in order to obtain the condition of the vehicle more conveniently and enable the vehicle to obtain rescue even when the vehicle fails, the driving module communicates with a remote service platform through a remote signal transmission module, the remote service platform is used for customer service to maintain and obtain the condition of the vehicle remotely, and the remote signal transmission module adopts 5G signal remote transmission.
Preferably, in order to improve the monitoring effect of the vehicle on the road condition, that is, improve the complex environment and emergency of the vehicle on the road condition, the calculation can be quickly made, and the correct parameters are obtained, so as to cope with the complex environment and emergency, the sensing module in S1 includes: the radar system comprises a radar laser module, a camera module and a sonar module; the radar laser module is combined with the GPS positioning module, draws a map through the determined vehicle body position information, and detects surrounding vehicle information; the camera module is used for shooting surrounding road conditions and collecting surrounding road condition images; the sonar module is used for detecting the distance between the sonar module and other vehicles on the road.
Preferably, in order to more quickly and effectively extract vehicle data, road data and parameters of the vehicle body itself around the vehicle body, the state classification module in S2 includes: the road condition and state extraction module and the vehicle body condition extraction module.
Preferably, the module used in S2 further comprises: the road condition state extraction module and the vehicle body state extraction module are both connected with the parameter adjustment module, the parameter adjustment module adjusts according to errors among standard parameters, extracted road condition parameters and vehicle body parameters, the road condition state extraction module is connected with the CNN recognition module and the path planning module, the CNN recognition module is used for recognizing road conditions and reasonably planning a driving path through the path planning module, and the path planning module is connected with the parameter adjustment module.
Preferably, in order to enable the vehicle to more accurately identify the road condition and reasonably plan the driving path during driving, the CNN identifying module includes: the device comprises a convolution layer feature extraction module, an activation layer module, a convergence layer module and an object tracking module, wherein the convolution layer feature extraction module uses different filters to extract different features from an input image; the activation layer module is connected with the convolution layer feature extraction module and used for controlling the opening of the target neuron; the convergence layer module is connected with the activation layer module and is used for compressing the space occupied by the feature map so as to reduce the number of parameters; the object tracking module is connected with the convergence layer module and is used for tracking the object according to the predicted object running track after the object is identified.
Specifically, the parameter adjustment module includes: the system comprises a vehicle body condition extraction module, a real parameter generation module, a parameter comparison module and an error calculation module which are sequentially connected, wherein the real parameter generation module is used for listing parameters obtained by the vehicle body condition extraction module, comparing the real parameters with standard parameters through the parameter comparison module, and calculating errors through the error calculation module.
Preferably, in order to further improve the calculation speed of the data and the accuracy of the calculation result, the parameter adjustment module is connected with an information base module, and the information base module includes a standard parameter input module, and the standard parameter input module is used for inputting and recording standard parameter data under various road conditions.
Preferably, in order to improve the detection efficiency and the feedback speed of the vehicle to the fault data, the parameter adjustment module further comprises a fault detection module, the fault detection module comprises an error threshold setting module, a threshold comparison module and an alarm module, the error threshold setting module is used for setting an error range between the standard parameter and the real parameter, the threshold comparison module is used for comparing whether the error between the opposite standard parameter and the real parameter is in the threshold range or not, and if not, the alarm module alarms.
The foregoing disclosure is only illustrative of the preferred embodiments of the present invention, but the embodiments of the present invention are not limited thereto, and any variations within the scope of the present invention will be apparent to those skilled in the art.
Claims (8)
1. An unmanned control method based on GPS positioning and 5G technology is characterized by comprising the following steps:
s1, determining position information of a vehicle body through a GPS positioning module, and acquiring vehicle running state data through a sensing module arranged on the vehicle;
s2, receiving state data through a state acquisition module connected and communicated with the sensing module, classifying the received state data by a state classification module connected with the state acquisition module, and classifying the running state data of the vehicle into a road condition state and a vehicle body state;
s3, receiving classification data of the state classification module according to the parameter adjustment module, and comparing the received road condition state and vehicle body state with corresponding standard parameters to calculate an error;
s4, transmitting the calculated error data to a driving module through a 5G signal, and adjusting driving parameters by the driving module according to the error data so as to operate and drive the vehicle to run;
the module in S2 further comprises: the system comprises a parameter adjustment module, a road condition state extraction module, a vehicle body state extraction module and a parameter adjustment module, wherein the road condition state extraction module and the vehicle body state extraction module are both connected with the parameter adjustment module, the parameter adjustment module adjusts according to errors among standard parameters, extracted road condition parameters and vehicle body parameters, the road condition state extraction module is connected with a CNN recognition module and a path planning module, the CNN recognition module is used for recognizing road conditions and reasonably planning a driving path through the path planning module, and the path planning module is connected with the parameter adjustment module;
the CNN identification module comprises:
the convolution layer feature extraction module is used for extracting different features from the input image by using different filters;
the activation layer module is connected with the convolution layer feature extraction module and used for controlling the opening of the target neuron;
the convergence layer module is connected with the activation layer module and is used for compressing the space occupied by the feature map so as to reduce the number of parameters;
and the object tracking module is connected with the convergence layer module and is used for tracking the object according to the predicted object running track after the object is identified.
2. The unmanned control method based on GPS positioning and 5G technology according to claim 1, wherein the driving module in S4 is provided on the vehicle for driving and operating the vehicle, and the parameters of the driving module include:
the steering parameters are steering wheel rotation angles and directions;
the accelerator parameter is the lifting height of the accelerator;
the brake parameter is the lifting height of the brake;
and the gear parameter is a gear of the vehicle body driving.
3. The unmanned control method based on GPS positioning and 5G technology according to claim 2, wherein the driving module in S4 communicates with a remote service platform through a remote signal transmission module, the remote service platform is used for customer service to remotely maintain and know the condition of the vehicle, and the remote signal transmission module adopts 5G signal remote transmission.
4. The unmanned control method according to claim 1, wherein the sensing module in S1 comprises:
the radar laser module is combined with the GPS positioning module, draws a map through the determined vehicle body position information, and detects surrounding vehicle information;
the camera module is used for shooting surrounding road conditions and collecting surrounding road condition images;
and the sonar module is used for detecting the distance between the sonar module and other vehicles on the road.
5. The unmanned control method according to claim 1, wherein the state classification module in S2 comprises: the road condition extraction module is used for extracting vehicle data and road data around the running of the vehicle body, and the vehicle condition extraction module is used for extracting parameters of the vehicle body.
6. The unmanned control method according to claim 1, wherein the parameter adjustment module comprises: the system comprises a vehicle body condition extraction module, a real parameter generation module, a parameter comparison module and an error calculation module which are sequentially connected, wherein the real parameter generation module is used for listing parameters obtained by the vehicle body condition extraction module, comparing the real parameters with standard parameters through the parameter comparison module, and calculating errors through the error calculation module.
7. The unmanned control method based on GPS positioning and 5G technology according to claim 6, wherein the parameter adjustment module is connected with an information base module, and the information base module comprises a standard parameter input module, and the standard parameter input module is used for inputting and recording standard parameter data under various road conditions.
8. The unmanned control method according to claim 7, wherein the parameter adjustment module further comprises a fault detection module, the fault detection module comprises an error threshold setting module, a threshold comparison module and an alarm module, the error threshold setting module is used for setting an error range between a standard parameter and a real parameter, the threshold comparison module is used for comparing whether an error between the opposite standard parameter and the real parameter is within the threshold range or not, and the alarm module alarms if the error is not within the threshold range.
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