CN105390018B - Underground parking lot intelligent guiding system based on machine learning - Google Patents
Underground parking lot intelligent guiding system based on machine learning Download PDFInfo
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- CN105390018B CN105390018B CN201510672225.6A CN201510672225A CN105390018B CN 105390018 B CN105390018 B CN 105390018B CN 201510672225 A CN201510672225 A CN 201510672225A CN 105390018 B CN105390018 B CN 105390018B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
- G08G1/146—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas where the parking area is a limited parking space, e.g. parking garage, restricted space
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096805—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
- G08G1/096811—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route where the route is computed offboard
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
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Abstract
The invention relates to an underground parking lot intelligent guiding system based on machine learning, which comprises a central console, a parking lot vacancy detection device and an electronic tag, wherein the electronic tag is fixed in a vehicle, and the central console is used for executing an optimal route planning algorithm, a dynamic route adjusting algorithm and a deep learning algorithm and communicating with the electronic tag; the parking lot vacancy detection device is used for communicating with the central console and reporting the occupation condition of the parking space; after the occupation condition of the parking space is collected by the parking lot vacancy detection device, the occupation condition is uploaded to the central control console, the central control console searches for the nearest vacant parking space according to the entering vehicle and binds the nearest vacant parking space with the vehicle, an optimal path algorithm is used for planning a route, and after the route is obtained, the electronic tag is instructed to carry out voice navigation and prepare for adjusting the route at any time. The invention can guide the car owner to quickly and safely find the empty parking space, and can reduce the complexity of the management of the parking lot after long-term use.
Description
Technical Field
The invention relates to parking space guidance in the technical field of application of the Internet of things, in particular to an underground parking lot intelligent guidance system based on machine learning.
Background
At present, the parking problem of underground parking lot is very outstanding, and many car owners can not find suitable parking stall and result in parking to become a very time-consuming thing. In addition, the complex terrain of underground parking often makes the vehicle owner unable to find the exit. If can provide a set of thing networking device that possesses the navigation function of fixing a position and help the car owner to park safely fast and go out the parking area smoothly, that will very big improvement life efficiency. Moreover, if the system has learning ability, it can tell the car owner at what time and in what parking lot and where there is a high probability of having a vacancy, which is of great significance for orderly parking and parking lot management.
Currently, machine learning is emerging, an algorithm is mature and easy to implement, while general indoor Positioning is cumbersome, a wireless sensor needs to be deployed indoors in advance, a certain degree of Channel modeling (Channel model construction) is performed, then an Anchor node (Anchor) deployed in advance is used for receiving a signal of a Mobile node (Mobile Tag, MT) and calculating a distance between the Mobile node and the MT, and a rough position of the Mobile node is calculated by using Trilateral Positioning (Trilateral Positioning) according to distance information obtained by at least three Anchor nodes.
At present, in the existing navigation mode of the parking lot, the road signs and the indicator lamps are mainly used, and most of the parking lots do not have navigation systems and can only search for vacant positions by car owners, so that the position searching time is increased, and internal jam of the parking lot is easily caused, and even car accidents occur.
Disclosure of Invention
The invention aims to solve the technical problem of providing an underground parking lot intelligent guiding system based on machine learning, which can guide a vehicle owner to quickly and safely find an empty parking space and can reduce the complexity of parking lot management after long-term use.
The technical scheme adopted by the invention for solving the technical problems is as follows: the system comprises a central console, a parking space detection device and an electronic tag with a voice navigation and acceleration sensor, wherein the electronic tag is fixed in a vehicle, and the central console is used for executing an optimal route planning algorithm, a dynamic route adjustment algorithm and a deep learning algorithm and communicating with the electronic tag; the parking lot vacancy detection device is used for communicating with the central console and reporting the occupation condition of the parking space; after the occupation condition of the parking space is collected by the parking lot vacancy detection device, the occupation condition is uploaded to the central control console, the central control console searches for the nearest vacant parking space according to the entering vehicle and binds the nearest vacant parking space with the vehicle, a route is planned by using an optimal path algorithm, and after the route is obtained, the electronic tag is commanded to carry out voice navigation.
The optimal route planning algorithm is used for vacancy navigation of vehicles entering the parking lot and takes the shortest route as a primary target.
The dynamic route adjustment algorithm is used for preventing collision among the automobiles when a plurality of entrances of a plurality of automobiles enter; at the route overlapping position, the electronic tag can send out voice prompt under the guidance of the central control console to warn the owner of the vehicle in front of the owner and prepare for avoiding; for vehicles which are not parked according to the regulations, the dynamic route adjusting algorithm is used for unbinding empty parking spaces and vehicles and realizing rebinding.
The deep learning algorithm is used for learning the distribution situation of the empty parking spaces in the parking lot and the parking situation of the car owner and giving a proper parking suggestion.
The parking lot vacancy detection device comprises detection equipment and communication equipment; the detection equipment is used for measuring by depending on a sensor or carrying out image recognition by adopting a camera; the communication equipment is in a sleep state under normal conditions, and when the central console receives a parking request or initializes, an activation instruction is sent to the communication equipment, so that the communication equipment enters a working state, and the occupation condition of the parking space is reported.
The electronic tag comprises a power supply module, an off-line voice sounding module, an acceleration sensor module and an RFID module; the power supply module is used for acquiring electric energy from a power supply inside the automobile and providing the electric energy for the electronic tag; the off-line voice sounding module is bound with the command of the central console, and after receiving a voice starting command sent by the central console, the off-line voice sounding module quickly searches for the bound voice and makes a voice response; the acceleration sensor module is used for acquiring three-dimensional acceleration information of the vehicle under an onboard coordinate system in real time and calculating the position of the vehicle through the central console; the RFID module is used for entrance and exit inspection and confirmation that a nearby vehicle will enter the parking lot.
When parking navigation is finished, the electronic tag is connected with a mobile terminal of a vehicle owner, and when the connection is disconnected, the electronic tag is reported to the central control console to prompt the vehicle owner to leave the vehicle, and the electronic tag automatically switches to a monitoring mode; when the vehicle owner returns to the parking lot, the electronic tag can establish connection with the vehicle owner mobile terminal again and send vehicle position information when detecting the vehicle owner mobile terminal within a certain range, and the vehicle owner is guided to find the vehicle.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: the invention positions the self through the inertial data, thereby rapidly realizing reliable inertial navigation. The invention also plans a locating route by using the central control console, and guides the car owner to orderly, safely and quickly find the parking space, leave the parking lot and reversely locate the car by using the special electronic tag. More importantly, after a period of time, the machine learning algorithm can remind the car owner of finding the parking space when and where every day, and the ordered management of car owner parking and the parking lot is greatly facilitated.
Drawings
FIG. 1 is an overall block diagram of an intelligent guidance system;
FIG. 2 is a schematic view of a car entering a parking lot navigation;
fig. 3 is a schematic diagram of the scheduling in parking navigation.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The embodiment of the invention relates to an underground parking lot intelligent guiding system based on machine learning, which comprises a central console, a parking lot vacancy detection device and an electronic tag with a voice navigation and acceleration sensor, wherein the electronic tag is fixed in a vehicle, and the central console is used for executing an optimal route planning algorithm, a dynamic route adjusting algorithm and a deep learning algorithm and communicating with the electronic tag; the parking lot vacancy detection device is used for communicating with the central console and reporting the occupation condition of the parking space; after the occupation condition of the parking space is collected by the parking lot vacancy detection device, the occupation condition is uploaded to the central control console, the central control console searches for the nearest vacant parking space according to the entering vehicle and binds the nearest vacant parking space with the vehicle, a route is planned by using an optimal path algorithm, and after the route is obtained, the electronic tag is commanded to carry out voice navigation.
The central console is a computing device with network connection and is mainly responsible for executing algorithms and communicating with the electronic tags.
The algorithm is executed by an optimal route planning algorithm, a dynamic route adjusting algorithm (scheduling algorithm) and a deep learning algorithm, wherein the optimal route planning algorithm is mainly used for vacancy navigation of vehicles entering a parking lot, and preferably, the shortest route is used as a primary target. The dynamic route adjustment algorithm (scheduling algorithm) is mainly used for preventing collision among automobiles when multiple entrances of multiple automobiles enter. At the route overlapping position, the electronic tag can send out voice prompt under the guidance of the central control console to warn the owner that a vehicle is in front of the owner and prepare for avoiding. In addition, the owner's behavior is free in nature and may not park as directed, and the central console's scheduling algorithm needs to solve this problem, resetting the vacancy binding when he occupies a vacancy that has been reserved by other vehicles. The deep learning algorithm is mainly used for learning the distribution situation of the empty parking spaces of the parking lot and the parking situation of the car owner, and giving appropriate parking suggestions, for example, at a certain time of a certain day, the empty parking spaces exist in certain parking lots, and even the specific position information of the empty parking spaces of the car owner is told, so that the parking efficiency is improved, and the management of the parking lot is facilitated. The communication with the electronic tag mainly comprises the synchronization of the tag position, the sending of a voice playing instruction to the tag by the central console and the like.
The parking lot vacancy detection device needs to be provided with network connection and can communicate with a central console at any time, namely, the parking lot vacancy detection device comprises detection equipment and communication equipment. The parking space occupation reporting system is mainly used for reporting the parking space occupation situation to a central control console, wherein the detection equipment can be used for measurement by depending on a sensor and can also be used for image recognition of a camera, and the type and the use method of the equipment are not limited in the invention. The communication device, preferably, should have reliability as a prerequisite for device selection. In order to reduce the energy consumption of the equipment, the equipment should not be in operation at all times, which is also very stressful for the network. Generally, the following trigger mechanism can be added inside the device: in a normal sleep state, when the central console receives a parking request or initializes, an activation instruction is sent to the equipment, so that the equipment enters a working state and reports parking space information.
The electronic tag is small, attractive, fixed in front of a driver seat of the automobile and prohibited to be detached, and the cruising ability of the electronic tag is at least more than 2 days, wherein the power module has a quick charging function and can directly obtain electric energy from an internal power supply of the automobile, the maximum current during the operation of the power module is limited to be below 5mA so as to ensure low power consumption and safety, the off-line voice sounding module is provided with an off-line voice library under all possible scenes and is bound with a central console command, and after a starting voice command sent by the central console is received, the off-line voice module quickly searches for the voice bound with the off-line voice module and makes a voice response, the acceleration sensor module, preferably an ADX L362 acceleration sensor of an ADI company, is used for obtaining three-dimensional parking information under an on-board coordinate system in real time, calculating the position of the automobile through the central console, and determining the optimal position of the automobile according to a wireless navigation protocol, and the optimal position of the RFID module, the wireless transmission protocol, and the optimal position of the parking lot is determined by a Bluetooth server and a Bluetooth recommendation of the wireless transmission protocol.
Fig. 1 is a main system block diagram of the booting system provided by the present invention.
The system comprises: the system comprises a central console 110, a direct anchor node 120, a parking space detection device 130, an electronic tag 140, other parking lots 150 and an indirect anchor node 160. Wherein:
the central console 110 is responsible for receiving signals of the parking space detection device 130 and the electronic tag 140 through the direct anchor node 120 and sending instructions to the signals; meanwhile, the central console will also obtain the parking space information of other parking lots through the indirect anchor node 160 for the need of machine learning.
The direct anchor node 120 is not only one, but a plurality of nodes need to be installed in order to achieve full coverage of the parking lot. The direct anchor node 120 functions as a signal relay station, and is responsible for information of the central console 110, the parking space detection device 130 and the electronic tag 140. In order to ensure the consistency of information and prevent collision when a plurality of direct anchor nodes 120 transmit information to the central console 110, each direct anchor node 120 needs to report its own ID information.
The parking space detection device 130 is in a passive detection state, i.e., in a sleep state, most of the time. The parking space condition is detected and reported to the center console 110 only when commanded by the center console 110. Every day, the parking space detecting device 130 actively sends the parking space information to the central console at the appointed time to check the correctness of the parking space information.
The electronic tag 140 is in a low power sleep state most of the time after being formally enabled, is activated when it detects a parking lot signal, and establishes communication with the central console 110. Thereafter inside the parking lot, the electronic tag 140 will communicate with the center console 110 frequently at a higher frequency, report raw data acquired by the acceleration sensor, the center console 110 quickly processes the information and obtains the location of the electronic tag 140, and continue to return to the navigation instruction. Skillfully, the electronic tag 140 can also be paired with the mobile phone of the owner of the vehicle. When the pairing is disconnected, the electronic tag will be switched to a monitoring state, and when the pairing is connected with the owner's mobile phone again, the electronic tag 140 will send the position of the owner to the owner's mobile phone, so that the owner can conveniently find the car, and get in contact with the central control console 110 to prepare for navigation of exiting the parking lot. When driving away from the parking lot, the electronic tag 140 will actively report the parking time and tell the parking space the parking fee.
As shown in fig. 2, a specific scenario will be described for the specific implementation of the guidance system:
first, all owners need to install the electronic tag 140 on the car and perform a pairing with the car by using a mobile phone. And after the pairing is successful, the electronic tag is formally started. (in the early stages of using an electronic tag, machine learning is still in the learning phase and will not work immediately.)
A plurality of direct anchor nodes are arranged in the parking lot, wherein the direct anchor nodes are installed on two sides near each entrance and exit, and meanwhile, the direct anchor nodes are also installed at the central position of the parking lot, so that the whole parking lot is ensured to be in a signal coverage area.
When the automobile 1 and the automobile 2 with the electronic tags are close to two entrances and exits of a parking lot, the electronic tags are awakened from a sleep state by broadcasting of the direct anchor nodes, then the electronic tags are communicated with the direct anchor nodes, acceleration sensor data are sent to a central console by the direct anchor nodes, the central console checks the condition of the empty parking places in the parking lot, if the number of the empty parking places is larger than or equal to the number of the automobiles, the automobiles are prompted to enter the parking lot, if the number of the empty parking places is smaller than the number of the automobiles, the tags in the automobiles are randomly selected to prompt the entering, the rest automobiles prompt that the parking places are insufficient, parking place information of the nearby parking lot is provided, and meanwhile the nearby parking lot is informed by the indirect. In addition, the electronic tag also transmits the door numbers of the parking lots where the automobiles 1 and 2 are close to each other, and the door numbers are recorded in order to count the traffic flow of each door and prepare training data for machine learning. When the automobile 1 and the automobile 2 drive into the parking lot, the RFID module is activated, the electronic tag sends a navigation request command to the central control console, the central control console runs the shortest path planning algorithm immediately, the empty parking space 1 is bound with the automobile 1, the empty parking space 2 is bound with the automobile 2, a path is calculated, the navigation command is returned, and dynamic scheduling is started at the same time. After receiving the navigation command, the electronic tag matches the response voice from the off-line voice navigation library, starts voice navigation, communicates with the direct anchor node at a higher frequency, and the direct anchor node transmits the information back to the central console. The dynamic scheduling will be described later by taking fig. 3 as an example.
When the automobile 1 and the automobile 2 reach the appointed vacant parking spaces, the central console firstly requires the parking space detection equipment to check the corresponding vacant parking spaces through the direct anchor node and reports and verifies whether the parking is correct or not, then informs the electronic tag to stop navigation, and sends out voice of navigation completion. At the moment, the electronic tag is automatically connected with the mobile phone of the vehicle owner, and when the mobile phone signal cannot be detected, the electronic tag enters a monitoring state. To this step, the parking navigation is already completed.
However, many times, especially when we have arrived at an unfamiliar parking lot, such as when we have gone to a strange mall for shopping, we then stop the car in the parking lot under the mall. When we finish shopping and go back to the parking lot again, we can not find our car due to the unfamiliar terrain. Referring to fig. 2, the car finder wants to find his car, i.e. the occupied space in fig. 2. In the system, the vehicle owner only needs to walk to the center, and after the vehicle owner arrives at a certain place, the corresponding electronic tag can sense the mobile phone of the vehicle owner, and then the tag transmits the position information of the vehicle to the mobile phone through the Bluetooth to guide the vehicle owner to find the vehicle. Meanwhile, the electronic tag can immediately continue to send data to the central console through the direct anchor node, and the central console calculates to obtain the position information.
After the car owner gets on the car, the car searching navigation is finished, if the position begins to change, the central console sends a navigation command through the direct anchor node, and the electronic tag navigates the car owner to the nearest entrance after receiving the navigation command. When the vehicle owner arrives at the entrance, the RFID module is triggered again, the electronic tag sends a payment request to the central console through the direct anchor node, the central console returns after calculation, and the vehicle owner is told the parking fee after receiving the electronic tag and returns the number of the entrance and the exit of the parking lot when the vehicle owner leaves. Again, this number is used for training samples of the center console's machine learning algorithm. And the vehicle owner leaves the parking lot after payment is finished, and when the electronic tag can not detect the signal of the direct anchor node any more, the electronic tag automatically enters a sleep state.
It is particularly stated that, after the central console learns for a period of time by using the machine learning algorithm, each time the owner leaves the parking lot, the electronic tag will remind the owner of the vacant parking lot at the next time in the next day with a high probability of having vacant parking spaces, and the reminding will be sent again after the next movement of the automobile. When the car owner is close to the parking lot each time, the car owner can be reminded of selecting to leave the parking lot at which time through the electronic tag, and the traffic flow peak can be avoided.
Next, a detailed description will be given of dynamic scheduling with reference to fig. 3:
as shown in FIG. 3, cars 1 and 2 are ready to enter the parking lot through entrance A and entrance E, respectively, and have been bound to parking spaces 1 and 2 through the central console, where ◎ indicates empty parking spaces and others are occupied parking spaces, it is clear that both cars 1 and 2 must pass through inflection point G to reach empty parking spaces.
At this time, the car 1 should stop at the position of the parking space 1 according to the binding rule and the navigation instruction. However, the owner of the vehicle does not necessarily stop according to the navigation instruction, and in a conventional way, the owner of the vehicle tends to stop to the right, so that the vehicle 1 goes against the navigation instruction and stops at the position of the parking space 2. When the central console finds that the car 1 deviates from the navigation instruction, it will communicate the voice command of the wrong route to the electronic tag through the direct anchor node, and the electronic tag will then prompt the car owner that the car has deviated from the predetermined route. When the owner is found to stop at a parking space bound by the automobile 2, the central console orders the equipment detection device to check and report the vacancy condition, after checking, the vacancy and the automobile 2 are immediately unbound, the vacancy corresponding to the automobile 1 is bound with the automobile 2, and meanwhile, the navigation information of the electronic tag is updated through the direct anchor node, and the parking space is bound again.
Claims (6)
1. An underground parking lot intelligent guiding system based on machine learning comprises a central console, a parking lot vacancy detection device and an electronic tag with a voice navigation and acceleration sensor, wherein the electronic tag is fixed in a vehicle; the parking lot vacancy detection device is used for communicating with the central console and reporting the occupation condition of the parking space; after the occupation condition of the parking space is collected by the parking lot vacancy detection device, uploading the occupation condition to the central control console, and the central control console searches for the nearest vacant parking space according to the entering vehicle to be bound with the vehicle, plans a route by using an optimal path algorithm, and commands the electronic tag to carry out voice navigation after obtaining the route; the dynamic route adjustment algorithm is used for preventing collision among the automobiles when a plurality of entrances of a plurality of automobiles enter; at the route overlapping position, the electronic tag can send out voice prompt under the guidance of the central control console to warn the owner of the vehicle in front of the owner and prepare for avoiding; for vehicles which are not parked according to the regulations, the dynamic route adjusting algorithm is used for unbinding empty parking spaces and vehicles and realizing rebinding.
2. The machine-learning based intelligent guidance system for underground parking lots according to claim 1, wherein the optimal routing algorithm is used for vacancy navigation of vehicles entering the parking lots with the shortest route as a primary target.
3. The intelligent guidance system for underground parking lots based on machine learning as claimed in claim 1, wherein the deep learning algorithm is used for learning the distribution of empty spaces in the parking lots and the parking situation of car owners and giving appropriate parking advice.
4. The machine-learning-based intelligent guidance system for underground parking lots according to claim 1, wherein the parking lot vacancy detection apparatus comprises a detection device and a communication device; the detection equipment is used for measuring by depending on a sensor or carrying out image recognition by adopting a camera; the communication equipment is in a sleep state under normal conditions, and when the central console receives a parking request or initializes, an activation instruction is sent to the communication equipment, so that the communication equipment enters a working state, and the occupation condition of the parking space is reported.
5. The machine-learning-based intelligent guidance system for underground parking lots according to claim 1, wherein the electronic tag comprises a power supply module, an offline voice sounding module, an acceleration sensor module and an RFID module; the power supply module is used for acquiring electric energy from a power supply inside the automobile and providing the electric energy for the electronic tag; the off-line voice sounding module is bound with the command of the central console, and after receiving a voice starting command sent by the central console, the off-line voice sounding module quickly searches for the bound voice and makes a voice response; the acceleration sensor module is used for acquiring three-dimensional acceleration information of the vehicle under an onboard coordinate system in real time and calculating the position of the vehicle through the central console; the RFID module is used for entrance and exit inspection and confirmation that a nearby vehicle will enter the parking lot.
6. The machine learning-based intelligent guidance system for underground parking lots according to claim 1, wherein when parking navigation is finished, the electronic tag is connected with a mobile terminal of a vehicle owner, and when the connection is disconnected, the electronic tag is reported to the central control console to prompt the vehicle owner to leave the vehicle, and the electronic tag automatically switches to a monitoring mode; when the vehicle owner returns to the parking lot, the electronic tag can establish connection with the vehicle owner mobile terminal again and send vehicle position information when detecting the vehicle owner mobile terminal within a certain range, and the vehicle owner is guided to find the vehicle.
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