CN114200937A - 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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- CN114200937A CN114200937A CN202111509408.8A CN202111509408A CN114200937A CN 114200937 A CN114200937 A CN 114200937A CN 202111509408 A CN202111509408 A CN 202111509408A CN 114200937 A CN114200937 A CN 114200937A
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- 238000001514 detection method Methods 0.000 claims description 9
- 230000008054 signal transmission Effects 0.000 claims description 8
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- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 3
- 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 acquisition module connected and communicated with the sensing module receives the state data, and the state classification module connected with the state acquisition module classifies the received state data, so that the driving state data of the vehicle is classified into a road condition state and a vehicle body state; receiving the classification data of the state classification module according to a parameter adjustment module, comparing the received road condition state and vehicle body state with corresponding standard parameters, and calculating 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 driving, in particular to an unmanned driving control method based on GPS positioning and 5G technology.
Background
The unmanned automobile is one of intelligent automobiles, also called as a wheeled mobile robot, and mainly depends on an intelligent driving instrument which is mainly a computer system in the automobile to realize the purpose of unmanned driving, and in real life, GPS positioning is mainly used for a technology for remotely positioning and monitoring people, pets, automobiles and equipment which move in real time.
The 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, the precision is not enough, namely, the ability of data extraction and planning route to the road conditions is relatively poor, only can snatch vehicle and topography situation around through the mode of making a video recording, cause the data bulk of extracting too big, can't come with the comparison of standard parameter according to road conditions state and automobile body state, calculate the error, more can't adjust the parameter of driving module according to the error, lead to unmanned precision not enough, there is the problem that the error is too big when taking place danger.
Disclosure of Invention
The invention aims to provide an unmanned control method based on GPS positioning and 5G technology 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 the position information of the vehicle body through a GPS positioning module, and acquiring vehicle driving state data through a sensing module arranged on the vehicle;
s2, receiving the state data through a state acquisition module connected and communicated with the sensing module, classifying the received state data through a state classification module connected with the state acquisition module, and classifying the driving state data of the vehicle into a road condition state and a vehicle body state;
s3, receiving the classification data of the state classification module according to the parameter adjustment module, comparing the received road condition state and vehicle body state with the corresponding standard parameters, and calculating 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, disposed on the vehicle, for driving and operating the vehicle, the parameters of the driving module include: the steering parameters are the rotation angle and the direction of a steering wheel; the accelerator parameter is the lifting height of an accelerator; the brake parameter is the lifting height of the brake; and the gear parameter is the gear of the vehicle body in running.
Furthermore, 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 obtain 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 the 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 vehicle body, and the vehicle body condition extraction module is used for extracting parameters of the vehicle body.
Further, the method also 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 between standard parameters, extracted road condition parameters and vehicle body parameters, the road condition state extraction module is connected with a CNN identification module and a path planning module, the CNN identification module is used for identifying road conditions and reasonably planning a driving path by the path planning module, and the path planning module is connected with the parameter adjustment module.
Further, the CNN identification module includes: a convolutional layer feature extraction module for extracting different features from the input image by using different filters; the activation layer module is connected with the convolutional layer feature extraction module and is used for controlling the starting of the target neuron; the convergence layer module is connected with the activation layer module and used for compressing the space occupied by the feature mapping graph so as to reduce the number of parameters; and the object tracking module is connected with the convergence layer module and tracks the object according to the predicted object running track after the object is identified.
Further, the parameter adjusting module comprises: the real parameter generating module is used for listing the parameters obtained by the vehicle body condition extracting module, the real parameters are compared with the standard parameters through the parameter comparing module, and errors are calculated through the error calculating module.
Furthermore, the parameter adjusting module is connected with an information base module, 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.
Furthermore, 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 an error between the standard parameter and the real parameter is in the threshold range, and if not, the alarm module gives an alarm.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the sensing module is used for acquiring vehicle form state data, the state acquisition module is used for acquiring the state data, the state data is classified by the state classification module and is classified into the road condition state and the vehicle body state, the standard parameters are compared with the road condition state and the vehicle body state, the error is calculated, and the parameters of the driving module are adjusted according to the error.
2. According to the invention, through the road condition state extraction module and the CNN identification module, data compression processing is performed, the workload of the system is reduced, the system operation is smoother, and therefore, the surrounding vehicles are identified more accurately.
3. According to the invention, the threshold range of the error can be flexibly set through the set fault detection module, and early warning is given out when the parameters cannot be automatically corrected by the driving module, so that a driver can be ensured to be timely adjusted to be in a control state, and the driving safety is improved.
4. According to the invention, through the remote service platform and the remote signal transmission module, the transmission of the 5G signal is carried out through the remote signal transmission module, so that the speed is higher, the communication between the remote service platform and a driver is more convenient, the long-term tracking of the driving module is facilitated, the situation that the error cannot be found in time when the error occurs is prevented, and the later-stage big data collection and the timely adjustment of the system of the driving module are more facilitated.
Drawings
FIG. 1 is a schematic diagram of the unmanned control method of the present invention;
FIG. 2 is a schematic diagram of a CNN identification module of the present invention;
FIG. 3 is a schematic diagram of a parameter adjustment module of the present invention;
FIG. 4 is a schematic diagram of a fault detection module of the present invention;
FIG. 5 is a schematic view of the placement of the driver modules of the unmanned control method of the present invention;
FIG. 6 is a schematic diagram of a sensing module of the present invention.
Detailed Description
The following describes in detail an embodiment of the present invention with reference to fig. 1 to 6. In the description of the present invention, it is to 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 those shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The terms "first", "second" and "first" 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 defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of the present invention, "a plurality" means two or more unless otherwise specified.
It should be noted that the circuit connections involved in the present invention all adopt a conventional circuit connection manner, and no innovation is involved.
Examples
As shown in fig. 1 to 6, an unmanned control method based on GPS positioning and 5G technology includes:
s1, determining the position information of the vehicle body through a GPS positioning module, and acquiring vehicle driving state data through a sensing module arranged on the vehicle;
s2, receiving the state data through a state acquisition module connected and communicated with the sensing module, classifying the received state data through a state classification module connected with the state acquisition module, and classifying the driving state data of the vehicle into a road condition state and a vehicle body state;
s3, receiving the classification data of the state classification module according to the parameter adjustment module, comparing the received road condition state and vehicle body state with the corresponding standard parameters, and calculating 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 a vehicle and used for driving and operating the vehicle, the parameters of the driving module include a steering parameter, an accelerator parameter, a brake parameter, and a shift parameter, and the steering parameter is a steering angle and a steering direction of a steering wheel; the accelerator parameter is the lifting height of an accelerator; the brake parameter is the lifting height of the brake; the gear parameter is the gear of the vehicle body in running.
Preferably, in order to more conveniently know the condition of the vehicle and enable the vehicle to obtain rescue when the vehicle breaks down, 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 know the condition of the vehicle, 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, to improve that the vehicle can quickly make a calculation in a complex environment and an emergency on the road condition, so as to obtain correct parameters, so as to cope with the complex environment and the emergency, the sensing module in S1 includes: the system comprises a radar laser module, a camera module and a sonar module; the radar laser module is combined with the GPS positioning module, a map is drawn according to the determined position information of the vehicle body, and the information of surrounding vehicles is detected; 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 extract vehicle data, road data, and parameters of the vehicle body itself around the vehicle body in a faster and more efficient manner, the state classification module in S2 includes: the road condition extraction module and the vehicle body condition extraction module.
Preferably, the module used in S2 further includes: 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 between standard parameters, extracted road condition parameters and vehicle body parameters, the road condition state extraction module is connected with a CNN identification module and a path planning module, the CNN identification module is used for identifying road conditions and reasonably planning a driving path by 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 recognize the road condition and reasonably plan the driving path during the driving process, the CNN recognition module includes: the system comprises a convolutional layer feature extraction module, an active layer module, a convergence layer module and an object tracking module, wherein the convolutional layer feature extraction module uses different filters to extract different features from an input image; the activation layer module is connected with the convolutional layer feature extraction module and used for controlling the starting of a target neuron; the convergence layer module is connected with the activation layer module and used for compressing the space occupied by the feature mapping graph so as to reduce the number of parameters; and the object tracking module is connected with the convergence layer module and tracks the object according to the predicted object running track after the object is identified.
Specifically, the parameter adjusting module includes: the real parameter generating module is used for listing the parameters obtained by the vehicle body condition extracting module, the real parameters are compared with the standard parameters through the parameter comparing module, and errors are calculated through the error calculating module.
Preferably, in order to further improve the calculation speed of the data and the accuracy of the calculation result, the parameter adjusting module is connected with an information base module, 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.
Preferably, in order to improve the detection efficiency and the feedback speed of the vehicle for fault data, the parameter adjusting 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 and setting whether an error between the standard parameter and the real parameter is within the threshold range, and if not, the alarm module gives an alarm.
Although the preferred embodiments of the present invention have been disclosed, the embodiments of the present invention are not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (10)
1. An unmanned control method based on GPS positioning and 5G technology is characterized by comprising the following steps:
s1, determining the position information of the vehicle body through a GPS positioning module, and acquiring vehicle driving state data through a sensing module arranged on the vehicle;
s2, receiving the state data through a state acquisition module connected and communicated with the sensing module, classifying the received state data through a state classification module connected with the state acquisition module, and classifying the driving state data of the vehicle into a road condition state and a vehicle body state;
s3, receiving the classification data of the state classification module according to the parameter adjustment module, comparing the received road condition state and vehicle body state with the corresponding standard parameters, and calculating 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.
2. The unmanned control method based on GPS positioning and 5G technology as claimed in claim 1, wherein the driving module in S4 is arranged on the vehicle for driving and operating the vehicle, and the parameters of the driving module include:
the steering parameters are the rotation angle and the direction of a steering wheel;
the accelerator parameter is the lifting height of an accelerator;
the brake parameter is the lifting height of the brake;
and the gear parameter is the gear of the vehicle body in running.
3. The unmanned aerial vehicle control method based on GPS positioning and 5G technology as claimed in 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 vehicle conditions, and the remote signal transmission module adopts 5G signal remote transmission.
4. The unmanned control method based on GPS positioning and 5G technology as claimed in 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 the 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 based on GPS positioning and 5G technology as claimed in 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 vehicle body, and the vehicle body condition extraction module is used for extracting parameters of the vehicle body.
6. The unmanned control method based on GPS positioning and 5G technology as claimed in claim 5, wherein the module 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 between standard parameters, extracted road condition parameters and vehicle body parameters, the road condition state extraction module is connected with a CNN identification module and a path planning module, the CNN identification module is used for identifying road conditions and reasonably planning a driving path by the path planning module, and the path planning module is connected with the parameter adjustment module.
7. The unmanned control method based on GPS positioning and 5G technology as claimed in claim 6, wherein the CNN identification module comprises:
a convolutional layer feature extraction module for extracting different features from the input image by using different filters;
the activation layer module is connected with the convolutional layer feature extraction module and is used for controlling the starting of the target neuron;
the convergence layer module is connected with the activation layer module and used for compressing the space occupied by the feature mapping graph so as to reduce the number of parameters;
and the object tracking module is connected with the convergence layer module and tracks the object according to the predicted object running track after the object is identified.
8. The unmanned control method based on GPS positioning and 5G technology as claimed in claim 6, wherein the parameter adjustment module comprises: the real parameter generating module is used for listing the parameters obtained by the vehicle body condition extracting module, the real parameters are compared with the standard parameters through the parameter comparing module, and errors are calculated through the error calculating module.
9. The unmanned control method based on GPS positioning and 5G technology as claimed in claim 8, wherein the parameter adjustment module is connected with an information base module, 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.
10. The unmanned aerial vehicle control method based on GPS positioning and 5G technology as claimed in claim 9, 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 the standard parameter and the real parameter, the threshold comparison module is used for comparing whether an error between the standard parameter and the real parameter is within the threshold range, and if not, the alarm module gives an alarm.
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