KR20220107492A - Parking control system using parking robot based on artificial intelligence - Google Patents

Abstract

The present invention relates to a parking control system using a parking robot based on artificial intelligence. According to the present invention, the parking control system using a parking robot based on artificial intelligence includes: a learning unit for learning a learning model that sets the parking spot of a vehicle parked in the parking lot and the expected exit time as an input coefficient and sets the parking spot of a vehicle entering the parking lot as an output coefficient; a database unit for storing vehicle license plates, average parking time, entry time, and expected exit time for each vehicle of the currently parked vehicle for the corresponding parking lot; a vehicle recognition unit for recognizing the vehicle number using a camera installed at the parking lot entrance and predicting the exit time, when a vehicle enters the parking lot; a decision unit for determining the parking spot of the currently entered vehicle by applying the number of currently parked vehicles expected to leave at the same time to the learning model; and a control unit for controlling to move an entering vehicle to a determined parking spot using a parking robot. According to the present invention, by determining the parking spot by considering the information of vehicles entering the parking lot and the information of vehicles parked in the parking lot through artificial intelligence, congestion between vehicles entering or leaving the same time zone is eliminated. In addition, the inconvenience of double parking and the burden of vehicle damage can be reduced by using a parking robot.

Description

A parking control system using a parking robot based on artificial intelligence

The present invention relates to a parking control system using a parking robot based on artificial intelligence, and more particularly, determining a parking spot by considering information on vehicles entering a parking lot and information on vehicles parked in a parking lot through artificial intelligence, It relates to a parking control system using a parking robot based on artificial intelligence that uses one or more parking robots to park a corresponding vehicle in a determined parking spot.

As the industry develops and the number of vehicles increases, the parking problem of vehicles also becomes a serious situation. In particular, the shortage of parking space is more serious not only in downtown areas, but also in apartments without parking facilities such as underground parking lots and parking towers, general residential areas and shopping malls due to the ever-increasing number of vehicles.

Due to the lack of such a parking space, so-called double parking in which parking is overlapped with a vehicle already parked in a parking zone is routinely performed. However, if the driver who parked first needs to get out, the driver of the double-parked vehicle has to come out and move the vehicle, or the driver who parked first pushes the double-parked vehicle to move it, causing damage to the vehicle or personal injury. Various problems may occur, such as, causing disputes between borrowers.

More specifically, if the driver who parked first pushes the double-parked vehicle to move it, there is no means to properly stop the vehicle from the outside after pushing the double-parked vehicle starts moving, so A collision or, in severe cases, a human accident may occur.

In addition, when vehicles use a public parking lot, it is difficult for car owners to grasp an empty parking space in real time, and accordingly, the congestion level in the parking lot increases and the time it takes to park is increased.

In addition, the area of the parking space in the parking lot is very narrow due to the increasing number of vehicles. Therefore, when the owner parks the vehicle and gets on or off the vehicle, there is a problem that damage to the vehicle such as a doorknob may occur.

The technology that is the background of the present invention is disclosed in Korean Patent Publication No. 10-1943628 (published on January 30, 2019).

The technical task to be achieved by the present invention is to determine a parking spot in consideration of the vehicle information that entered the parking lot and the vehicle information parked in the parking lot through artificial intelligence, and park the vehicle in the determined parking spot using one or more parking robots. It is related to a parking control system using a parking robot based on artificial intelligence.

According to an embodiment of the present invention for achieving this technical task, in a parking control system using a parking robot based on artificial intelligence, a parking spot and an expected departure time of a vehicle parked in the parking lot are set as input coefficients, and the A learning unit for learning a learning model that sets the parking spot of an entering vehicle as an output coefficient, a vehicle license plate of a vehicle currently parked in the corresponding parking lot, a database unit for storing the average parking time, entry time, and expected exit time for each vehicle; When a vehicle enters the parking lot, the vehicle recognition unit that recognizes the vehicle number and predicts the departure time using the camera installed at the entrance of the parking lot, and the number of currently parked vehicles that are expected to exit at the same time period are applied to the learning model. It includes a determining unit for determining a parking spot of the entered vehicle, and a control unit for controlling the entering vehicle to move to the determined parking spot using a parking robot.

The learning unit may be trained to determine the parking seat of the vehicle currently entering the parking lot so that a distance from the currently parked vehicle having the same expected exit time as the expected exit time of the vehicle currently entering the parking lot is increased.

The learning unit, when there is one or more previously parked vehicles having the same expected exit time as the expected exit time of the entered vehicle, the sum of the distance difference from the current location of the one or more previously parked vehicles among the vacant parking spots is You can decide on the largest parking spot.

The learning unit, if there is no pre-parked vehicle having the same expected departure time as the expected departure time of the entered vehicle, the longer the average parking time of the entered vehicle, the longer it is determined as a parking seat farther from the exit, , the shorter the average parking time, the more likely it is to determine a parking spot closer to the exit.

When there are a plurality of parking robots and each parking robot pulls out a vehicle in the same time period, a movement line for un-parking of the parking robot may be learned so that a collision does not occur between the respective parking robots.

The parking robot is implemented with a smaller height and narrower width than a vehicle pallet for mounting a vehicle, and if there is a vehicle in and out, it can move under the vehicle pallet to move the vehicle pallet on which the vehicle is mounted in an orthogonal operation.

As described above, according to the present invention, the congestion level between vehicles entering or leaving the same time zone is resolved by determining a parking spot in consideration of the vehicle information entering the parking lot and the vehicle information parked in the parking lot through artificial intelligence.

In addition, by using the parking robot to park or take out the vehicle in the parking lot, the hassle of double parking and the burden of damage to the vehicle can be reduced, and damage to the vehicle such as the doorcock due to the narrow parking space interview in the parking lot can be prevented. can

1 is a view for explaining a parking control system using a parking robot based on artificial intelligence according to an embodiment of the present invention.
2A is a view for explaining a parking robot according to an embodiment of the present invention.
2B is a view for explaining a state in which a vehicle is mounted on a parking robot according to an embodiment of the present invention.
3 is a block diagram showing the configuration of a parking control system using a parking robot based on artificial intelligence according to an embodiment of the present invention.
4 is a flowchart illustrating a parking control method using an artificial intelligence-based parking control system according to an embodiment of the present invention.
FIG. 5 is a diagram for explaining step S420 of FIG. 4 .
FIG. 6A is a diagram for explaining step S440 of FIG. 4 .
6B is a view for explaining determination of a parking seat in step S440 of FIG. 4 .

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily carry out the present invention. However, the present invention may be embodied in several different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part "includes" a certain component, it means that other components may be further included, rather than excluding other components, unless otherwise stated.

Then, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those of ordinary skill in the art to which the present invention pertains can easily implement them.

Hereinafter, a parking control system 100 using a parking robot 300 based on artificial intelligence according to an embodiment of the present invention will be described with reference to FIGS. 1 to 2B .

1 is a view for explaining a parking control system using a parking robot based on artificial intelligence according to an embodiment of the present invention.

As shown in FIG. 1 , the parking control system 100 using the parking robot 300 according to the embodiment of the present invention is connected to the camera 200 and the parking robot 300 through a network.

First, the parking control system 100 receives a vehicle number through the camera 200 installed at the entrance of the parking lot, and the camera 200 connects to the network by wire or wirelessly and transmits an image of a vehicle entering the parking lot. can be implemented as

In addition, the parking robot 300 is implemented as a device for moving the vehicle entering the parking lot to the parking spot determined by the parking control system 100 .

Hereinafter, a parking robot 300 according to an embodiment of the present invention will be described with reference to FIGS. 2A and 2B .

2A is a view for explaining a parking robot according to an embodiment of the present invention, and FIG. 2B is a view for explaining a state in which a vehicle is mounted on the parking robot according to an embodiment of the present invention.

As shown in FIGS. 2A and 2B , the parking robot 300 is implemented with a lower height and narrower width than the vehicle pallet 310 for mounting the vehicle 400 .

If there is a vehicle 400 entering and leaving the parking lot, the parking robot 300 may move under the vehicle pallet 310 and lift the vehicle 400 mounted vehicle pallet 310 to park. Then, the parking robot 300 comes out from under the vehicle pallet 310 by lowering the height. In this case, the parking robot 300 may move in an orthogonal manner.

That is, the parking control system 100 receives the number of the vehicle 400 entering the parking lot through the camera 200 installed at the entrance of the parking lot, and determines the parking spot using the information of the vehicle 400, The robot 300 controls to park the corresponding vehicle 400 in the determined parking spot.

Hereinafter, the configuration of the parking control system 100 using the parking robot 300 according to the embodiment of the present invention will be described with reference to FIG. 3 .

3 is a block diagram showing the configuration of a parking control system using a parking robot based on artificial intelligence according to an embodiment of the present invention.

As shown in FIG. 3 , the parking control system 100 using a parking robot according to an embodiment of the present invention includes a learning unit 110 , a database unit 120 , a vehicle recognition unit 130 , a determining unit 140 and a control unit 150 .

First, the learning unit 110 trains a learning model that sets a parking spot and an expected departure time of a vehicle parked in a parking lot as an input coefficient and sets a parking spot of a vehicle entering the parking lot as an output coefficient.

At this time, the learning unit 110 trains the learning model to determine the parking seat of the vehicle entering the parking lot so that the distance from the parking vehicle having the same expected exit time as the expected exit time of the vehicle entering the parking lot increases.

Next, the database unit 120 stores the license plate of the vehicle parked in the corresponding parking lot, the average parking time, the entry time, and the expected departure time for each vehicle.

At this time, the database unit 120 stores the average parking time history for vehicles that have previously used the corresponding parking lot, and uses the vehicle number parked in the corresponding parking lot, the entry time, and the previously used average parking time. Predict and store the expected departure time of the vehicle.

Next, when the vehicle enters the parking lot, the vehicle recognition unit 130 uses a camera installed at the entrance to the parking lot to recognize the vehicle number and predict the vehicle's exit time.

Here, the vehicle recognition unit 130 recognizes the license plate of the vehicle through the image taken by the camera, records the vehicle's parking entry time, and uses the average parking time of the corresponding vehicle number stored in the database unit 120 . Predict the expected departure time of the vehicle.

Next, the determination unit 140 determines the parking position of the vehicle entering by applying the number of vehicles expected to leave in the same time period to the learning model.

At this time, the determination unit 140 learns to determine the parking seat of the vehicle entering the parking lot so that the currently entered vehicle and the expected departure time are far from the vehicle parked in the same parking lot by applying to the learning model learned using artificial intelligence. do.

Next, the control unit 150 controls the vehicle to move to the determined parking spot by using the parking robot.

In this case, when there are a plurality of parking robots 300 that enter and exit the parking lot, the controller 150 may control the movement lines of the plurality of parking robots 300 so as not to overlap each other.

Hereinafter, a parking control method using the parking control system will be described with reference to FIGS. 4 to 6B .

4 is a flowchart illustrating a parking control method using a parking control system according to an embodiment of the present invention.

First, the learning unit 110 learns a learning model that outputs a parking spot of a vehicle entering a parking lot by inputting a parking spot and an expected departure time of a vehicle previously parked in the parking lot (S410).

At this time, if there is a parked vehicle having the same expected exit time as the expected exit time of the vehicle entering the parking lot, the learning unit 110 is a parked vehicle having the same expected exit time as the parking seat of the vehicle currently entering the parking lot Train the learning model to determine a point far away from .

In more detail, when there is one or more previously parked vehicles having the same expected exit time as the expected exit time of the vehicle currently entering the parking lot, the learning unit 110 is a parking lot in which the parking space of the vehicle currently entering the parking lot is empty. The learning model is trained so that the sum of the difference between the current location and distance of one or more previously parked vehicles among seats is determined to be the largest point.

And, if there is no pre-parked vehicle having the same expected exit time as the expected exit time of the vehicle entering the parking lot, the learning unit 110 is located further from the exit as the average parking time of the vehicle entering the parking lot is longer. The learning model is trained to determine the parking spot that is located in the parking lot, and the shorter the average parking time, the closer to the parking spot from the exit.

Next, the database unit 120 stores the license plate of the vehicle parked in the corresponding parking lot, the average parking time, the entry time, and the expected departure time for each vehicle (S420).

In this case, the database unit 120 stores the vehicle number and average parking time of the vehicle for which the vehicle has a history of using the corresponding parking lot before.

In addition, the database unit 120 predicts and stores the expected departure time from the entry time and the pre-stored average parking time according to the vehicle number currently using the corresponding parking lot.

Hereinafter, vehicles parked in the corresponding parking lot stored in the database unit 120 will be described with reference to FIG. 5 .

FIG. 5 is a diagram for explaining step S420 of FIG. 4 .

As shown in FIG. 5, if it is assumed that the average parking time of the vehicle defense "23 or 4355" vehicle that has used the corresponding parking lot before is 9 hours, the vehicle "23 or 4355" enters at 9 am and enters the parking lot at 6 pm It can be predicted that the vehicle will be pulled out at , and vehicles "23 or 4355" are currently parked in A-7 position.

In addition, the average parking time of the vehicle number "11th group 9874", which has used the parking lot before, is 1 hour, and the entry time is 12 pm, so the expected departure time is 1 pm, and the current "11th group 9874" vehicle is parked in C-1, and the average parking time of the vehicle number "No. 93 1065" is 5 hours, and the entry time is 1 pm, so the expected departure time is 6 pm, and the current "No. 93 1065" vehicle is parked in B-1, the average parking time of the vehicle number "83 bar 5127" is 3 hours, and the entry time is 2 pm, so the expected departure time is 5 pm, and the current "83 bar 5127" vehicle is parked at D-4.

That is, the database unit 120 stores the average parking time, entry time, expected exit time, and vehicle location of the vehicle for each vehicle number with respect to the vehicle parked in the corresponding parking lot.

Next, when the vehicle enters the parking lot, the vehicle recognition unit 130 recognizes the vehicle number of the currently entered vehicle through the camera installed at the entrance and predicts the expected departure time (S430).

In this case, the vehicle recognition unit 130 may recognize the number of the vehicle through the image and image of the vehicle taken in and out of the camera, and simultaneously record the time the recognized vehicle entered the parking lot.

In addition, the vehicle recognition unit 130 predicts the expected departure time by using the average parking time of the corresponding vehicle number in the parking lot previously stored in the database unit 120 .

Next, the determination unit 140 applies the number of vehicles expected to leave at the same time period to the learning model to determine the parking spot of the currently entered vehicle ( S440 ).

At this time, when the occupancy of the parked vehicle in the parking lot is less than 50% of the total available parking space, the determination unit 140 does not use a learning model based on artificial intelligence, and may allocate the seat at random. .

In addition, the determiner 140 may predict the number of vehicles to be pulled out for each time zone from the database unit 120 based on the pre-stored expected un-parking time for the vehicle parked in the corresponding parking lot.

For example, if it is assumed that the expected departure time of the vehicle currently entering is 6:00, and there is a parked vehicle that leaves within 6:05, the determination unit 140 determines the parking lot of the currently entering vehicle. It determines a parking spot through a learning model to park at a far distance from the vehicle.

In this case, the range of the expected departure time period may be set to 5 minutes, and the set minute interval may be changed.

For example, if it is assumed that the expected departure time of one vehicle is 1:00 pm, the range of the same expected departure time period may include other vehicles that get out of the vehicle from 1:00 pm to 1:05 pm.

In addition, the determining unit 140 determines a parking spot through the learning model so that a vehicle currently entering the parking lot is parked at a point where the sum of the distance difference from a vehicle having the same expected departure time is the largest.

Hereinafter, parking seat determination will be described with reference to FIGS. 6A and 6B .

FIG. 6A is a view for explaining step S440 of FIG. 4 , and FIG. 6B is a view for explaining determination of a parking seat at step S440 of FIG. 4 .

As shown in Fig. 6a, the vehicle 500a that has entered the current parking lot is placed on the vehicle pallet 310, and the vehicle 500b having the same expected departure time as the vehicle 500a that entered the parked vehicle 500b, 500c, 500d) Assuming that there are three vehicles, the determining unit 140 determines a parking seat of the vehicle 500a that has entered the parking lot from among ⓐ to ⓙ which are vacant seats in the corresponding parking lot.

Here, in the parking lot of FIG. 6A , since the occupancy of the parked vehicles is 50% or more of the total available parking spaces, the determination unit 140 may determine the parking space using an artificial intelligence-based learning model.

Then, the determining unit 140 learns to park in the position where the sum of the differences between the positions and distances of the vehicles 500b, 500c, and 500d having the same expected departure time with respect to the parking seat of the vehicle 500a entering the parking lot is the largest. It is determined through the model, and the entered vehicle 500a may be parked in one of the vacant seats ⓐ to ⓙ.

As shown in FIG. 6B , the determination unit 140 places the largest sum of the differences between the positions and distances of the vehicles 500b, 500c, and 500d having the same expected departure time with respect to the parking seat of the entered vehicle 500a. The parking space is determined through the learning model to be parked in

And, when there is no previously parked vehicle having the same expected exit time as the expected exit time of the vehicle currently entering the parking lot, the determination unit 140 determines a parking spot according to the average parking time.

For example, assuming that the time the vehicle enters the parking lot is 1 o'clock, the expected departure time is 3 o'clock, and there is no parked vehicle having the same expected departure time as the vehicle entering the parking lot, the determination unit 140 is Learn to determine a parking spot far from the exit compared to vehicles with an average parking time of 1 hour among vehicles parked in the parking lot

You can train the model. In addition, the determination unit 140 determines a parking seat through the learning model to determine a parking seat near the exit compared to a vehicle having an average parking time of 5 hours among vehicles parked in the parking lot.

Next, the control unit 150 controls the vehicle entering the parking lot using the parking robot to move from the determining unit 140 to the parking seat determined through the learning model (S450).

That is, when there are a plurality of parking robots 300 and each parking robot 300 un-parks one or more previously parked vehicles having the same expected un-parking time period, the controller 150 controls the movement lines between the parking robots 300 to overlap. Control to avoid collision.

As described above, according to an embodiment of the present invention, by determining a parking spot in consideration of vehicle information entering the parking lot and vehicle information parked in the parking lot through artificial intelligence, the degree of congestion between vehicles entering or leaving the same time zone is resolved. In addition, by using the parking robot to park or take out the vehicle in the parking lot, the hassle of double parking and the burden of damage to the vehicle can be reduced, and damage to the vehicle such as the doorcock due to the narrow parking space interview in the parking lot can be prevented. can

Although the present invention has been described with reference to the embodiment shown in the drawings, which is merely exemplary, it will be understood by those skilled in the art that various modifications and equivalent other embodiments are possible therefrom. Accordingly, the true technical protection scope of the present invention should be defined by the technical spirit of the appended claims.

100: parking control system, 110: database unit,
120: vehicle recognition unit, 130: learning unit,
140: control unit, 200: camera,
300: parking robot, 310: vehicle pallet,
400: vehicle, 500a: vehicle entering the parking lot
500b, 500c, 500d: Parked vehicles with the same expected departure time

Claims (6)

In a parking control system using a parking robot based on artificial intelligence,
A learning unit that trains the learning model by setting the parking spot and expected departure time of a vehicle parked in the parking lot as an input coefficient and setting the parking spot of a vehicle entering the parking lot as an output coefficient;
A database unit that stores the license plate, average parking time, entry time, and expected exit time of the currently parked vehicle for the corresponding parking lot for each vehicle;
When a vehicle enters the parking lot, a vehicle recognition unit that uses a camera installed at the entrance of the parking lot to recognize the vehicle number of the entered vehicle and predict the departure time;
A determining unit that determines the parking spot of the currently entered vehicle by applying the number of currently parked vehicles expected to depart in the same time period to the learning model, and
A parking control system comprising a control unit for controlling the vehicle to move to the determined parking seat by using a parking robot. According to claim 1,
The learning unit,
A parking control system that is learned to determine the parking spot of a vehicle entering the parking lot so that the distance from the parking vehicle having the same expected departure time as the expected departure time of the vehicle entering the parking lot increases. 3. The method of claim 2,
The learning unit,
When there is one or more previously parked vehicles having the same expected departure time as the expected departure time of the entered vehicle,
A parking control system that determines the parking spot with the largest sum of distances from the positions of one or more previously parked vehicles among vacant parking spots. 3. The method of claim 2,
The learning unit,
If there is no pre-parked vehicle having the same expected departure time as the expected departure time of the entered vehicle,
The longer the average parking time of the entered vehicle, the more distant the parking spot from the exit is, and the shorter the average parking time, the closer the parking space is to the exit. According to claim 1,
The learning unit,
If there are a plurality of the parking robots, and each parking robot in the same time zone pulls out the vehicle,
A parking control system that learns a movement line for taking out of the parking robot so that a collision does not occur between each parking robot. According to claim 1,
The parking robot is
It is implemented with a smaller height and narrower width than a vehicle pallet for mounting a vehicle.
A parking control system that moves under the vehicle pallet and moves the vehicle pallet on which the vehicle is mounted in an orthogonal operation when there is a vehicle entering or leaving.

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