CN115022837A - Automatic driving automobile control method and system with unmanned aerial vehicle as aerial base station - Google Patents
Automatic driving automobile control method and system with unmanned aerial vehicle as aerial base station Download PDFInfo
- Publication number
- CN115022837A CN115022837A CN202210606041.XA CN202210606041A CN115022837A CN 115022837 A CN115022837 A CN 115022837A CN 202210606041 A CN202210606041 A CN 202210606041A CN 115022837 A CN115022837 A CN 115022837A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- automobile
- automatic driving
- state information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000012545 processing Methods 0.000 claims abstract description 35
- 230000002159 abnormal effect Effects 0.000 claims abstract description 21
- 238000012544 monitoring process Methods 0.000 claims description 16
- 230000008447 perception Effects 0.000 claims description 8
- 238000013500 data storage Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 abstract description 14
- 238000004891 communication Methods 0.000 abstract description 10
- 230000000875 corresponding effect Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 2
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005562 fading Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64C—AEROPLANES; HELICOPTERS
- B64C39/00—Aircraft not otherwise provided for
- B64C39/02—Aircraft not otherwise provided for characterised by special use
-
- G—PHYSICS
- G08—SIGNALLING
- G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
- G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
- G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
- H04W88/02—Terminal devices
- H04W88/04—Terminal devices adapted for relaying to or from another terminal or user
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Aviation & Aerospace Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Human Computer Interaction (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention belongs to the technical field of wireless communication, and particularly relates to an automatic driving automobile control method and system with an unmanned aerial vehicle as an aerial base station; the method comprises the following steps: initializing an unmanned aerial vehicle group; the automatic driving automobile sends a request packet to the unmanned aerial vehicle; receiving signals from the unmanned aerial vehicle, acquiring and storing state information of the unmanned aerial vehicle and the automobile; processing the stored state information from the unmanned aerial vehicle to obtain processing data; judging whether the automobile running environment is safe or not according to the processing data from the unmanned aerial vehicle, if so, sending a reply packet to the automobile, and otherwise, sending an adjusting signal to the automobile; the automobile controls the driving state according to the reply packet or the adjusting signal; the slave unmanned aerial vehicle monitors the states of the unmanned aerial vehicle and the automobile according to the state information, and if an abnormal condition is found, the unmanned aerial vehicle cluster deploys the position again; after the position is redeployed, when the performance of the system is judged to be optimal, the control process is repeated; the invention provides guarantee for the safe operation of the automatic driving automobile and has high practicability.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to an automatic driving automobile control method and system with an unmanned aerial vehicle as an aerial base station.
Background
In recent years, with the rapid development of technologies such as artificial intelligence and internet of things, the unmanned aerial vehicle technology has entered a rapid development period, and from the initial application to reconnaissance, monitoring and communication in the military field, nowadays, unmanned aerial vehicles have formed intelligent aircrafts combined by multiple technologies such as flight control, network communication and power supply functions, and even have been able to serve as aerial base stations to provide communication services for user equipment. Unmanned aerial vehicle in the past is single rifle horse executive task usually, but also faces the risk of trouble damage simultaneously, and many unmanned aerial vehicle cooperation executive task can enough reduce the task degree of difficulty, can be convenient for redistribute again when breaking down, also just because this characteristic, unmanned aerial vehicle can cooperate through sharing information to realize the promotion of executive task efficiency.
The technology of the automatic driving automobile is continuously updated since the emergence of the world, the automatic driving automobile is operated in a gradual trial and trial, and as the technology is improved day by day, the fact that the unmanned driving automobile is on the stage is not contended. Nowadays, a trend of the overall development of a communication network is intellectualization, and a completely fixed ground base station cannot efficiently and accurately control an automatic driving automobile under the condition that the signal quality in the area is not ideal due to large local flow of the automatic driving automobile, and is lack of flexibility; the unmanned aerial vehicle is used as an aerial base station, corresponding decisions can be made according to the characteristics of the surrounding environment, the capabilities of automatic path finding and target identification are fully exerted, real-time information sharing can be realized among different unmanned aerial vehicles by means of a network, and the unmanned aerial vehicle can control an automatic driving automobile. Based on the future development trend, an unmanned aerial vehicle serving as an air base station automatic driving automobile control method is urgently needed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an automatic driving automobile control method and system with an unmanned aerial vehicle as an aerial base station, wherein the method comprises the following steps:
s1: initializing an unmanned aerial vehicle group; the unmanned aerial vehicle cluster comprises a master unmanned aerial vehicle and slave unmanned aerial vehicles;
s2: the automatic driving automobile sends a request packet to the unmanned aerial vehicle;
s3: receiving signals from the unmanned aerial vehicle, acquiring and storing state information of the unmanned aerial vehicle and the automobile;
s4: processing the stored state information from the unmanned aerial vehicle to obtain processing data;
s5: judging whether the automobile running environment is safe or not according to the processing data from the unmanned aerial vehicle, if so, sending a reply packet to the automobile, and otherwise, sending an adjusting signal to the automobile;
s6: the automobile controls the driving state according to the reply packet or the adjusting signal;
s7: the slave unmanned aerial vehicle monitors the states of the unmanned aerial vehicle and the automobile according to the state information, if an abnormal condition is found, the unmanned aerial vehicle cluster deploys the position again, and if not, the step S2 is returned;
s8: and after the position is redeployed, judging whether the performance of the system is optimal, if so, returning to the step S2, and otherwise, redeploying the position by the unmanned aerial vehicle group.
Preferably, initializing the drone swarm includes:
setting the coverage diameter of an unmanned aerial vehicle cluster, taking a main unmanned aerial vehicle as a center, distributing three slave unmanned aerial vehicles on a sector center with a central angle of 120 degrees according to an even distribution principle, calculating the distance between the slave unmanned aerial vehicles and the main unmanned aerial vehicle according to the coverage diameter of the unmanned aerial vehicle cluster, wherein the distances between the three slave unmanned aerial vehicles and the main unmanned aerial vehicle are equal; each unmanned aerial vehicle is 200m away from the ground.
Preferably, the status information includes: the position, the jitter degree and the signal transmitting power of the unmanned aerial vehicle, and the signal transmitting power, the position and the running speed of the automatic driving automobile.
Preferably, the processing the stored state information from the drone includes: the distance between the autonomous vehicles and the surrounding intrinsic objects are calculated based on the state information.
Preferably, the judging whether the operating environment of the vehicle is safe includes: setting a safe distance between automobiles and setting a safe distance between an automobile and an inherent object; and comparing whether the distance between the automatically driven automobiles and the distance between the automobiles and the inherent object are both greater than the corresponding safe distance, if so, judging that the running environment is safe, otherwise, judging that the running environment is unsafe.
Preferably, the abnormal condition includes: the number of the automatically driven vehicles exceeds the set threshold value N max (ii) a The position of the slave unmanned aerial vehicle changes without receiving a command from the master unmanned aerial vehicle, and the change comprises coordinates, height and angle; the jitter degree exceeds the preset jitter level safe 。
Preferably, the process of determining the performance of the system comprises: setting a signal-to-interference-and-noise ratio threshold and a signal intensity threshold; calculating the signal-to-interference-and-noise ratio of a receiving end of the automatic driving automobile; calculating the signal intensity of a receiving end of the automatic driving automobile; judging whether the signal-to-interference-and-noise ratio of the receiving end of the automatic driving automobile reaches a signal-to-interference-and-noise ratio threshold value or not and whether the signal strength of the receiving end of the automatic driving automobile reaches a signal strength threshold value or not; if both are satisfied, the performance of the system reaches the best, otherwise, the performance of the system fails to reach the best.
An autonomous vehicle control system with unmanned aerial vehicles as aerial base stations, comprising: the system comprises an information perception module, a data storage module, a central data processing module, a monitoring module, a decision-making module and a central decision-making module;
the information perception module is used for acquiring state information of the unmanned aerial vehicle and the automobile;
the data storage module is used for storing state information of the unmanned aerial vehicle and the automobile;
the central data processing module is used for processing state information of the unmanned aerial vehicle and the automobile to obtain processed data;
the monitoring module is used for monitoring the state of the automobile and the unmanned aerial vehicle according to the state information of the unmanned aerial vehicle and the automobile and judging whether an abnormal condition exists or not;
the decision module is used for judging whether the automobile running environment is safe or not according to the processing data and sending a decision instruction;
the central decision-making module is used for deploying the positions of the unmanned aerial vehicle group according to the abnormal conditions and judging the performance of the system at the same time.
The invention has the beneficial effects that: the invention designs an automatic driving automobile control method with an unmanned aerial vehicle as an aerial base station, which uses the unmanned aerial vehicle as the aerial base station to realize the control of an automatic driving automobile on the bottom surface; different from a completely fixed ground base station, the automatic driving automobile can not be efficiently and accurately controlled under the condition that the local flow of the automatic driving automobile is large, so that the signal quality in the area is not ideal, and the flexibility is lacked; the invention uses the unmanned aerial vehicle as the aerial base station for service, fully utilizes the high flexibility of the unmanned aerial vehicle, can provide high-quality base station service for the automatic driving automobile through the cooperative processing of the unmanned aerial vehicles, can timely process the abnormal condition of the automatic driving automobile and redeploy the base station according to a new scene, really realizes taking the user as the center, provides guarantee for the safe operation of the automatic driving automobile, has high practicability and wide application prospect.
Drawings
FIG. 1 is a flow chart of an automatic driving vehicle control method using an unmanned aerial vehicle as an air base station according to the present invention;
FIG. 2 is a schematic view of a scene of an automatic driving vehicle control method using an unmanned aerial vehicle as an air base station according to the present invention;
fig. 3 is a schematic structural diagram of an autopilot control system using an unmanned aerial vehicle as an air base station according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an automatic driving automobile control method with an unmanned aerial vehicle as an aerial base station, which provides communication service for an automatic driving automobile, meets the driving requirement of the automatic driving automobile, utilizes resources as reasonably as possible and embodies the intelligent characteristic of a future communication network; the method is characterized in that the equipment in the application scene is an automatic driving automobile and an unmanned aerial vehicle; as shown in fig. 1, the method includes the following:
s1: and initializing the unmanned aerial vehicle group.
As shown in fig. 2, the drone swarm includes a master drone and slave drones, and preferably, one drone swarm is composed of one master drone and three slave drones. The main unmanned aerial vehicle is used as the center, and the slave unmanned aerial vehicles are distributed to respective control positions to form a star topology structure. The unmanned aerial vehicle is used as an aerial base station, the coverage diameter of the unmanned aerial vehicle cluster is set for the low-altitude field, and preferably, the coverage range of the unmanned aerial vehicle cluster is preset in a circle with the diameter of 20 kilometers; taking a master unmanned aerial vehicle as a center, distributing three slave unmanned aerial vehicles on a sector center with a central angle of 120 degrees according to an even distribution principle, calculating the distance between each slave unmanned aerial vehicle and the master unmanned aerial vehicle according to the coverage diameter of the unmanned aerial vehicle cluster, wherein the distances between the three slave unmanned aerial vehicles and the master unmanned aerial vehicle are equal; each unmanned aerial vehicle is 200m away from the ground. The unmanned aerial vehicle is provided with an omnidirectional customized antenna for receiving signals sent by the automatic driving automobile.
The unmanned aerial vehicle starts an information sensing module, and the information sensing module is used for acquiring state information of the unmanned aerial vehicle and an automatic driving automobile.
S2: the automatic driving automobile sends a request packet to the unmanned aerial vehicle;
the automatic driving automobile is provided with an omnidirectional customized antenna, and a request packet is sent to the aerial base station, wherein the request packet contains the position and the running information of the automatic driving automobile and a next moving command.
S3: receiving signals from the unmanned aerial vehicle, acquiring and storing state information of the unmanned aerial vehicle and the automobile;
the communication mode between the air base station and the automatic driving automobile is realized based on millimeter wave communication. The information perception module acquires state information of the unmanned aerial vehicle and the automatic driving automobile and stores the state information in the data storage module; the state information includes: the position, the jitter degree and the signal transmitting power of the unmanned aerial vehicle, and the signal transmitting power, the position and the running speed of the automatic driving automobile.
The monitoring module is started when the signal is received from the unmanned aerial vehicle, the monitoring module monitors the unmanned aerial vehicle and the automatic driving automobile according to the state information acquired by the information sensing module, the monitoring data comprise the coordinate, the height, the angle, the shaking condition and the cruising condition of the unmanned aerial vehicle, and the running speed, the track and the strength of the sent signal of the automatic driving automobile.
S4: and processing the stored state information from the unmanned aerial vehicle to obtain processing data.
The central data processing module on the slave unmanned aerial vehicle processes the state information stored in the data processing module, specifically, the distance between the automatic driving vehicles is calculated according to the state information, and the distance between the automatic driving vehicles and the surrounding inherent objects is calculated, so that the processed data is obtained.
S5: and judging whether the automobile running environment is safe or not according to the processing data from the unmanned aerial vehicle, if so, sending a reply packet to the automobile, and otherwise, sending an adjusting signal to the automobile.
The decision-making module from unmanned aerial vehicle acquires the processing data of central data processing module, judges whether the operating environment of the automatic driving automobile is safe according to the processing data, and the judging process comprises the following steps: comparing whether the distance between the automatically driven vehicles and the distance between the vehicle and the inherent object are both greater than the corresponding safe distance (safe distance d between the automatically driven vehicles) 1 Safe distance d between the vehicle and the body 2 ) (ii) a If the judgment result is larger than the preset threshold, the operation environment is judged to be safe, otherwise, the operation environment is judged to be unsafe.
If the judgment result is safe, a reply packet is sent to the automatic driving automobile from the unmanned aerial vehicle; if the judgment result is unsafe, sending an adjusting signal from the unmanned aerial vehicle to the automatic driving automobile, wherein the adjusting signal contains adjusting instructions such as speed reduction, lane change and the like; these command data are encapsulated in the adjustment signal and transmitted to the designated vehicle via the antenna.
For example, when the vehicle speed exceeds the safe driving speed of the lane, the adjustment signal includes a speed reduction command, and when the vehicle speed is lower than the safe driving speed of the lane, the adjustment signal includes a speed increase command. The addition and subtraction amplitudes are adjusted by a dichotomy, namely, the deceleration is firstly reduced to the middle value of the safe speed, if the deceleration is needed, the deceleration is reduced to the minimum value of the safe driving speed and the middle value of the speed at the moment, and the like.
If the adjusting signal contains a lane changing instruction, the situation that the front of the automatic driving automobile is blocked is indicated, and the lane changing can be selected to reach the destination, and the automatic driving automobile randomly selects one lane from the variable lanes to run according to the lane changing instruction.
S6: the automobile controls the driving state according to the reply packet or the adjusting signal.
Setting adjustment time, controlling the running state of the automatic driving automobile according to a reply packet or an adjustment signal, if the reply packet is received, indicating that the current state is normal, keeping the current running state, and if the adjustment signal is received, performing state adjustment according to a corresponding adjustment instruction, thereby achieving the purpose of controlling the running state of the automatic driving automobile.
S7: and the slave unmanned aerial vehicle monitors the states of the unmanned aerial vehicle and the automobile according to the state information, if an abnormal condition is found, the unmanned aerial vehicle cluster deploys the position again, and if not, the step S2 is returned.
In the driving process of the automatic driving automobile, the monitoring module of the slave unmanned aerial vehicle monitors the activity condition of a target object in real time based on the data sensed by the sensing module so as to find out abnormal data conditions in time; the abnormal conditions include: in the same area of the same time period, the number of the automatically driven automobile vehicles exceeds the set threshold value N max (ii) a The position of the slave unmanned aerial vehicle changes without receiving a command from the master unmanned aerial vehicle, and the change comprises coordinates, height and angle; the shaking degree exceeds the preset shaking level through the perception of a special sensor safe 。
If the abnormal situation is found, the central data processing module of the slave unmanned aerial vehicle processes the abnormal data found by the monitoring module and feeds back the abnormal data processing result to the master unmanned aerial vehicle; a central decision module of the main unmanned aerial vehicle is provided with a solution algorithm for dealing with abnormal case, and outputs corresponding action instructions according to different execution actions in different case starting algorithms; the output action instruction is sent to a central decision module of the master unmanned aerial vehicle or the slave unmanned aerial vehicle, and the central decision module controls the unmanned aerial vehicle to execute the action and relocate the position; if no abnormal condition is found, the current position deployment is kept, after the adjustment time is over, the automatic driving automobile sends the request packet to the slave unmanned aerial vehicle again, namely, the processes of the steps S2 to S7 are carried out again, and the control process is repeated continuously. The driving of the automatic driving automobile is further flexibly controlled by monitoring the whole control process.
S8: after the position is redeployed, whether the performance of the system is optimal or not is judged, if so, the step S2 is returned, and if not, the position is redeployed by the unmanned aerial vehicle group.
Requirements to guarantee the best performance of the system: ensuring that the SINR of the receiving end of the automatic driving automobile is not lower than the preset threshold SINR min Secondly, the signal intensity of the receiving end of the automatic driving automobile is ensured not to be lower than the preset threshold RSSI min . The formula for calculating the signal-to-interference-and-noise ratio and the signal strength is as follows:
RSSI i ＝T x-power +L pass +R gain +S gain
wherein, P i Indicating the transmission power, P, of the i-th vehicle i′ Denotes the ith ′ Transmission power, SINR, of a vehicle i Representing the signal-to-interference-and-noise ratio of the signal sent by the ith automobile to the unmanned aerial vehicle; RSSI i Representing the strength of signals received by the ith automobile, wherein, I is more than or equal to 1 and less than or equal to M, M represents the total number of the automatic driving automobiles existing in the space-time, and the set of M represents I, and I is more than or equal to { I |1 and less than or equal to I and less than or equal to M }; t is x-power The power transmitted by the aerial base station is shown, assuming that the transmission power of the three drones is the same, L pass Denotes the path loss, R gain Representing the reception gain, S gain Representing the system gain.
Under the condition that a channel model based on an unmanned aerial vehicle and an automatic driving automobile is a free space propagation model, a large-scale fading model is mainly considered, and the calculation formula of the path loss is as follows:
where d is the distance between the transmitting and receiving antennas, f is the carrier frequency used, and c represents the speed of light.
The process of judging the performance of the system comprises the following steps: setting SINR threshold min And signal strength threshold RSSI min (ii) a Calculating the signal-to-interference-and-noise ratio of a receiving end of the automatic driving automobile; calculating the signal intensity of a receiving end of the automatic driving automobile; judging whether the signal-to-interference-and-noise ratio of the receiving end of the automatic driving automobile reaches a signal intensity threshold value or not and whether the signal intensity of the receiving end of the automatic driving automobile reaches the signal intensity threshold value or not; if both are satisfied, the performance of the system reaches the best, otherwise, the performance of the system fails to reach the best.
If the judgment result is yes, the process returns to the step S2, namely, the automatic driving automobile sends the request packet to the slave unmanned aerial vehicle again, and the process is carried out again, otherwise, the unmanned aerial vehicle cluster deploys the position again.
As shown in fig. 3, the present invention further provides an autonomous driving vehicle control system using the unmanned aerial vehicle as an air base station, which is used for executing the above-mentioned method for controlling an autonomous driving vehicle using the unmanned aerial vehicle as an air base station; the system comprises: the system comprises an information perception module, a data storage module, a central data processing module, a monitoring module, a decision-making module and a central decision-making module;
the information perception module is used for acquiring state information of the unmanned aerial vehicle and the automobile;
the data storage module is used for storing state information of the unmanned aerial vehicle and the automobile;
the central data processing module is used for processing state information of the unmanned aerial vehicle and the automobile to obtain processed data;
the monitoring module is used for monitoring the state of the automobile and the unmanned aerial vehicle according to the state information of the unmanned aerial vehicle and the automobile and judging whether an abnormal condition exists or not;
the decision module is used for judging whether the automobile running environment is safe or not according to the processing data and sending a decision instruction;
the central decision-making module is used for deploying the positions of the unmanned aerial vehicle group according to the abnormal conditions and judging the performance of the system at the same time.
The invention designs an automatic driving automobile control method with an unmanned aerial vehicle as an aerial base station, which uses the unmanned aerial vehicle as the aerial base station to realize the control of an automatic driving automobile on the bottom surface; different from a completely fixed ground base station, the automatic driving automobile can not be efficiently and accurately controlled under the condition that the local flow of the automatic driving automobile is large, so that the signal quality in the area is not ideal, and the flexibility is lacked; the invention uses the unmanned aerial vehicle as the aerial base station for service, fully utilizes the high flexibility of the unmanned aerial vehicle, can provide high-quality base station service for the automatic driving automobile through the cooperative processing of the unmanned aerial vehicles, can timely process the abnormal condition of the automatic driving automobile and redeploy the base station according to a new scene, really realizes taking the user as the center, provides guarantee for the safe operation of the automatic driving automobile, has high practicability and wide application prospect.
The above-mentioned embodiments, which further illustrate the objects, technical solutions and advantages of the present invention, should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An automatic driving automobile control method with an unmanned aerial vehicle as an aerial base station is characterized by comprising the following steps:
s1: initializing an unmanned aerial vehicle group; the unmanned aerial vehicle cluster comprises a master unmanned aerial vehicle and slave unmanned aerial vehicles;
s2: the automatic driving automobile sends a request packet to the unmanned aerial vehicle;
s3: receiving signals from the unmanned aerial vehicle, acquiring and storing state information of the unmanned aerial vehicle and the automobile;
s4: processing the stored state information from the unmanned aerial vehicle to obtain processing data;
s5: judging whether the automobile running environment is safe or not according to the processing data from the unmanned aerial vehicle, if so, sending a reply packet to the automobile, and otherwise, sending an adjusting signal to the automobile;
s6: the automobile controls the driving state according to the reply packet or the adjusting signal;
s7: the slave unmanned aerial vehicle monitors the states of the unmanned aerial vehicle and the automobile according to the state information, if an abnormal condition is found, the unmanned aerial vehicle cluster deploys the position again, and if not, the step S2 is returned;
s8: after the position is redeployed, whether the performance of the system is optimal or not is judged, if so, the step S2 is returned, and if not, the position is redeployed by the unmanned aerial vehicle group.
2. The method of claim 1, wherein initializing the drone swarm comprises:
setting the coverage diameter of an unmanned aerial vehicle cluster, taking a main unmanned aerial vehicle as a center, distributing three slave unmanned aerial vehicles on a sector center with a central angle of 120 degrees according to an even distribution principle, calculating the distance between the slave unmanned aerial vehicles and the main unmanned aerial vehicle according to the coverage diameter of the unmanned aerial vehicle cluster, wherein the distances between the three slave unmanned aerial vehicles and the main unmanned aerial vehicle are equal; each unmanned aerial vehicle is 200m away from the ground.
3. The method of claim 1, wherein the status information comprises: the position, the jitter degree and the signal transmitting power of the unmanned aerial vehicle, and the signal transmitting power, the position and the running speed of the automatic driving automobile.
4. The method of claim 1, wherein the processing the stored status information from the drone includes: the distance between the autonomous vehicles and the surrounding intrinsic objects are calculated based on the state information.
5. The method of claim 1, wherein determining whether the operating environment of the vehicle is safe comprises: setting a safe distance between automobiles and setting a safe distance between an automobile and an inherent object; and comparing whether the distance between the automatically driven automobiles and the distance between the automobiles and the inherent object are both greater than the corresponding safe distance, if so, judging that the running environment is safe, otherwise, judging that the running environment is unsafe.
6. The method of claim 1, wherein the abnormal situation includes: in the same area of the same time period, the number of the automatically driven automobile vehicles exceeds the set threshold value N max (ii) a The position of the slave unmanned aerial vehicle changes without receiving a command from the master unmanned aerial vehicle, and the change comprises coordinates, height and angle; the jitter degree exceeds the preset jitter level safe 。
7. The method of claim 1, wherein the step of determining the performance of the system comprises: setting a signal-to-interference-and-noise ratio threshold and a signal intensity threshold; calculating the signal-to-interference-and-noise ratio of a receiving end of the automatic driving automobile; calculating the signal intensity of a receiving end of the automatic driving automobile; judging whether the signal-to-interference-and-noise ratio of the receiving end of the automatic driving automobile reaches a signal-to-interference-and-noise ratio threshold value or not and whether the signal strength of the receiving end of the automatic driving automobile reaches a signal strength threshold value or not; if both are satisfied, the performance of the system reaches the best, otherwise, the performance of the system fails to reach the best.
8. An unmanned aerial vehicle is as autopilot car control system of air basic station, its characterized in that includes: the system comprises an information perception module, a data storage module, a central data processing module, a monitoring module, a decision-making module and a central decision-making module;
the information perception module is used for acquiring state information of the unmanned aerial vehicle and the automobile;
the data storage module is used for storing state information of the unmanned aerial vehicle and the automobile;
the central data processing module is used for processing state information of the unmanned aerial vehicle and the automobile to obtain processed data;
the monitoring module is used for monitoring the state of the automobile and the unmanned aerial vehicle according to the state information of the unmanned aerial vehicle and the automobile and judging whether an abnormal condition exists or not;
the decision module is used for judging whether the automobile running environment is safe or not according to the processing data and sending a decision instruction;
the central decision-making module is used for deploying the positions of the unmanned aerial vehicle group according to the abnormal conditions and judging the performance of the system at the same time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210606041.XA CN115022837A (en) | 2022-05-31 | 2022-05-31 | Automatic driving automobile control method and system with unmanned aerial vehicle as aerial base station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210606041.XA CN115022837A (en) | 2022-05-31 | 2022-05-31 | Automatic driving automobile control method and system with unmanned aerial vehicle as aerial base station |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115022837A true CN115022837A (en) | 2022-09-06 |
Family
ID=83070505
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210606041.XA Pending CN115022837A (en) | 2022-05-31 | 2022-05-31 | Automatic driving automobile control method and system with unmanned aerial vehicle as aerial base station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115022837A (en) |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160088636A (en) * | 2015-01-16 | 2016-07-26 | 경북대학교 산학협력단 | Uav-guided ugv driving method and system |
| CN107678428A (en) * | 2017-09-25 | 2018-02-09 | 南京律智诚专利技术开发有限公司 | A kind of method of work of the vehicle automatic running system based on remote sensing |
| CN107871399A (en) * | 2016-12-27 | 2018-04-03 | 珠海市杰理科技股份有限公司 | Vehicular automatic driving system and method |
| CN109426255A (en) * | 2017-09-04 | 2019-03-05 | 中兴通讯股份有限公司 | Automatic driving vehicle control method, device and storage medium based on unmanned plane |
| CN109510655A (en) * | 2017-09-15 | 2019-03-22 | 中国科学院光电研究院 | Near space platform oriented mission formation networking distributed collaboration flight control method |
| CN110119147A (en) * | 2019-05-09 | 2019-08-13 | 深圳市速腾聚创科技有限公司 | Vehicular automatic driving method, apparatus, computer equipment and storage medium |
| CN110650039A (en) * | 2019-09-17 | 2020-01-03 | 沈阳航空航天大学 | Multimodal optimization-based network collaborative communication model for unmanned aerial vehicle cluster-assisted vehicle |
| US20200010095A1 (en) * | 2019-08-30 | 2020-01-09 | Lg Electronics Inc. | Method and apparatus for monitoring driving condition of vehicle |
| KR20200010994A (en) * | 2018-07-11 | 2020-01-31 | 삼성전자주식회사 | In-vehicle infotainment system communicating with unmanned ariel vehicle and method of operating the same |
| CN112203289A (en) * | 2020-04-26 | 2021-01-08 | 北京理工大学 | Aerial base station network deployment method for area coverage of cluster unmanned aerial vehicle |
| DE102020122758A1 (en) * | 2019-09-05 | 2021-03-11 | Ford Global Technologies, Llc | COLLABORATION BETWEEN A UAV AND AN AUTOMOBILE |
| CN112509321A (en) * | 2020-11-27 | 2021-03-16 | 佛山市墨纳森智能科技有限公司 | Unmanned aerial vehicle-based driving control method and system for urban complex traffic situation and readable storage medium |
| CN112817307A (en) * | 2020-12-30 | 2021-05-18 | 广州大学 | Vehicle auxiliary driving system and method based on unmanned aerial vehicle road perception |
| CN112977441A (en) * | 2021-03-03 | 2021-06-18 | 恒大新能源汽车投资控股集团有限公司 | Driving decision method and device and electronic equipment |
| CN113034981A (en) * | 2021-04-14 | 2021-06-25 | 北京航空航天大学 | Multi-relay unmanned aerial vehicle flight path planning method and system in uncertain channel environment and storage medium |
| CN113515121A (en) * | 2021-04-27 | 2021-10-19 | 东风汽车集团股份有限公司 | Intelligent driving fleet formation support system and method based on unmanned aerial vehicle |
| CN113993105A (en) * | 2021-10-21 | 2022-01-28 | 北京邮电大学 | Unmanned aerial vehicle three-dimensional deployment parameter setting method for urban hot spot area |
| CN113978482A (en) * | 2020-07-27 | 2022-01-28 | 现代自动车株式会社 | Automatic driving vehicle and emergency response method based on unmanned aerial vehicle |
| CN114248802A (en) * | 2021-10-28 | 2022-03-29 | 鄂尔多斯市普渡科技有限公司 | Method and device for improving safety of unmanned vehicle and storage medium |
| CN114355900A (en) * | 2021-12-16 | 2022-04-15 | 无锡中盾科技有限公司 | Cooperative operation method combining unmanned aerial vehicle and unmanned aerial vehicle |
-
2022
- 2022-05-31 CN CN202210606041.XA patent/CN115022837A/en active Pending
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160088636A (en) * | 2015-01-16 | 2016-07-26 | 경북대학교 산학협력단 | Uav-guided ugv driving method and system |
| CN107871399A (en) * | 2016-12-27 | 2018-04-03 | 珠海市杰理科技股份有限公司 | Vehicular automatic driving system and method |
| CN109426255A (en) * | 2017-09-04 | 2019-03-05 | 中兴通讯股份有限公司 | Automatic driving vehicle control method, device and storage medium based on unmanned plane |
| CN109510655A (en) * | 2017-09-15 | 2019-03-22 | 中国科学院光电研究院 | Near space platform oriented mission formation networking distributed collaboration flight control method |
| CN107678428A (en) * | 2017-09-25 | 2018-02-09 | 南京律智诚专利技术开发有限公司 | A kind of method of work of the vehicle automatic running system based on remote sensing |
| KR20200010994A (en) * | 2018-07-11 | 2020-01-31 | 삼성전자주식회사 | In-vehicle infotainment system communicating with unmanned ariel vehicle and method of operating the same |
| CN110119147A (en) * | 2019-05-09 | 2019-08-13 | 深圳市速腾聚创科技有限公司 | Vehicular automatic driving method, apparatus, computer equipment and storage medium |
| US20200010095A1 (en) * | 2019-08-30 | 2020-01-09 | Lg Electronics Inc. | Method and apparatus for monitoring driving condition of vehicle |
| DE102020122758A1 (en) * | 2019-09-05 | 2021-03-11 | Ford Global Technologies, Llc | COLLABORATION BETWEEN A UAV AND AN AUTOMOBILE |
| CN110650039A (en) * | 2019-09-17 | 2020-01-03 | 沈阳航空航天大学 | Multimodal optimization-based network collaborative communication model for unmanned aerial vehicle cluster-assisted vehicle |
| CN112203289A (en) * | 2020-04-26 | 2021-01-08 | 北京理工大学 | Aerial base station network deployment method for area coverage of cluster unmanned aerial vehicle |
| CN113978482A (en) * | 2020-07-27 | 2022-01-28 | 现代自动车株式会社 | Automatic driving vehicle and emergency response method based on unmanned aerial vehicle |
| CN112509321A (en) * | 2020-11-27 | 2021-03-16 | 佛山市墨纳森智能科技有限公司 | Unmanned aerial vehicle-based driving control method and system for urban complex traffic situation and readable storage medium |
| CN112817307A (en) * | 2020-12-30 | 2021-05-18 | 广州大学 | Vehicle auxiliary driving system and method based on unmanned aerial vehicle road perception |
| CN112977441A (en) * | 2021-03-03 | 2021-06-18 | 恒大新能源汽车投资控股集团有限公司 | Driving decision method and device and electronic equipment |
| CN113034981A (en) * | 2021-04-14 | 2021-06-25 | 北京航空航天大学 | Multi-relay unmanned aerial vehicle flight path planning method and system in uncertain channel environment and storage medium |
| CN113515121A (en) * | 2021-04-27 | 2021-10-19 | 东风汽车集团股份有限公司 | Intelligent driving fleet formation support system and method based on unmanned aerial vehicle |
| CN113993105A (en) * | 2021-10-21 | 2022-01-28 | 北京邮电大学 | Unmanned aerial vehicle three-dimensional deployment parameter setting method for urban hot spot area |
| CN114248802A (en) * | 2021-10-28 | 2022-03-29 | 鄂尔多斯市普渡科技有限公司 | Method and device for improving safety of unmanned vehicle and storage medium |
| CN114355900A (en) * | 2021-12-16 | 2022-04-15 | 无锡中盾科技有限公司 | Cooperative operation method combining unmanned aerial vehicle and unmanned aerial vehicle |
Non-Patent Citations (4)
| Title |
|---|
| 吴昭洁: "基于无人机辅助的高能效车辆精确定位机理" * |
| 李勇;: "人工智能发展推动信息安全范式转移――基于百度无人驾驶汽车的案例分析", 信息安全研究, no. 11, 5 November 2016 (2016-11-05) * |
| 薛冰冰;白子建;: "基于无人驾驶车辆特性的传统道路交通规划设计理念革新", 建材与装饰, no. 06, 25 February 2020 (2020-02-25) * |
| 谢博宇;李茂青;岳丽丽;陈启香;: "无人机-智能车队协同路径实时规划研究", 计算机工程与应用, no. 20 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6942909B2 (en) | Aerial repeater in collaborative MIMO system | |
| CN113645635A (en) | Design method of intelligent reflector-assisted high-energy-efficiency unmanned aerial vehicle communication system | |
| CN109866631B (en) | Unmanned aerial vehicle formation aerial wireless charging method | |
| CN111970709B (en) | Unmanned aerial vehicle relay deployment method and system based on particle swarm optimization algorithm | |
| CN112203289B (en) | Aerial base station network deployment method for area coverage of cluster unmanned aerial vehicle | |
| CN108270476B (en) | High-altitude communication system, method and device | |
| CN111711960A (en) | Energy efficiency perception unmanned aerial vehicle cluster three-dimensional deployment method | |
| CN112020001B (en) | Time slot resource allocation method for multi-station multi-machine system of unmanned aerial vehicle | |
| Pan et al. | Joint optimization of trajectory and resource allocation for time-constrained UAV-enabled cognitive radio networks | |
| CN113556189B (en) | Antenna adjusting method and device for unmanned aerial vehicle | |
| CN117270559A (en) | Unmanned aerial vehicle cluster deployment and track planning method based on reinforcement learning | |
| CN117412267B (en) | Communication method of unmanned aerial vehicle cluster network | |
| Zhang et al. | Joint computation offloading and trajectory design for aerial computing | |
| CN113776531A (en) | Multi-unmanned-aerial-vehicle autonomous navigation and task allocation algorithm of wireless self-powered communication network | |
| CN117676530A (en) | Multi-unmanned aerial vehicle searching and rescuing communication method and system based on intelligent cluster | |
| Yan et al. | Multi-UAV speed control with collision avoidance and handover-aware cell association: DRL with action branching | |
| CN115022837A (en) | Automatic driving automobile control method and system with unmanned aerial vehicle as aerial base station | |
| Huang et al. | Delay-oriented knowledge-driven resource allocation in sagin-based vehicular networks | |
| CN111796604A (en) | Light-load type remote cruise unmanned aerial vehicle group system | |
| He et al. | Intelligent terahertz medium access control (MAC) for highly dynamic airborne networks | |
| CN109597428B (en) | Unmanned aerial vehicle anti-collision method based on ZigBee network, unmanned aerial vehicle and server | |
| CN114448537A (en) | Compromise method for energy efficiency and spectrum efficiency in communication network | |
| CN114697975B (en) | Unmanned aerial vehicle cluster distributed deployment method for enhancing land wireless coverage | |
| Long et al. | AoI-aware Sensing Scheduling and Trajectory Optimization for Multi-UAV-assisted Wireless Backscatter Networks | |
| Zhao et al. | UAV deployment with flexible height under SINR constraint |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |