CN105390018A - 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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- CN105390018A CN105390018A CN201510672225.6A CN201510672225A CN105390018A CN 105390018 A CN105390018 A CN 105390018A CN 201510672225 A CN201510672225 A CN 201510672225A CN 105390018 A CN105390018 A CN 105390018A
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- parking
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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
Abstract
The invention relates to an underground parking lot intelligent guiding system based on machine learning. The underground parking lot intelligent guiding system comprises a central console, a parking lot empty parking space detecting device and an electronic label. The electronic label is fixed in a vehicle. The central console is used for executing an optimal route programming algorithm, a dynamic route adjusting algorithm and a deep learning algorithm and communicating with the electronic label. The parking lot empty parking space detecting device is used for communicating with the central console and reporting an occupation condition of the parking spaces. After the parking lot empty parking space detecting device acquires the occupation condition of the parking spaces, the parking lot empty parking space detecting device transmits the occupation condition of the parking spaces to the central console. The central console searches a nearest empty parking space according to the entered vehicle and binds the empty parking space with the vehicle, and furthermore programs the route according to the optimal route algorithm. After the route is acquired, the central console commands the electronic label to perform sound navigation and prepare for route adjustment anytime. The underground parking lot intelligent guiding system has functions of guiding a vehicle owner to find the empty parking space quickly and safely, and reducing parking lot management complexity through long-term use.
Description
Technical field
The parking stall that the present invention relates in Internet of Things applied technical field guides, and particularly relates to a kind of underground parking intelligent guidance system based on machine learning.
Background technology
Current, the parking problem of underground parking is very outstanding, and a lot of car owner cannot find suitable parking stall to cause stopping and become a thing very consuming time.In addition, the landform of underground parking complexity, often makes car owner to find an exit.If a set of internet of things equipment possessing location navigation function can be provided to help car owner fast and safely to stop and come in and go out smoothly parking lot, that will improve life efficiency greatly.And, if system has learning ability, can tell when car owner exists room in the greater probability that where has in what parking lot, and that is significant for orderly parking and parking lot management.
Current, machine learning is in the ascendant, and algorithm is comparatively ripe, easy realization, and general indoor positioning, then seem loaded down with trivial details, need in advance at indoor deployment wireless senser, and carry out Channel Modeling (ChannelModelConstruction) to a certain degree.Then the anchor node (Anchor) disposed in advance is utilized to receive mobile node (MobileTag, MT) signal also calculates distance between the two, by the range information that at least three anchor nodes obtain, three location, limits (TrilateralPositioning) are utilized to calculate the rough position of mobile node.But these methods in Channel Modeling, need channel to be divided into sighting distance (Line-of-Sight, LOS) and non line of sight (None-Line-of-Sight, NLOS) situation.But indoor environment is very complicated and do not have generality, the scene of LOS and NLOS cannot accurately be distinguished, and brings very large error thus, and the positional information of MT cannot accurately be calculated, and deviation is larger.Meanwhile, if indoor scene is complicated or shelter is more, the difficulty of disposing WS can significantly increase, location high expensive.
And at present, in the existing navigate mode in parking lot, mainly with road sign, pilot lamp is main, and most parking lot does not also have navigational system, and car owner oneself can only be leaned on to grope to find room, this not only adds and seek bit time, and easily cause inner part of parking lot to block up, even get into an accident.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of underground parking intelligent guidance system based on machine learning, can guide car owner fast and safely find empty parking space, Long-Time Service can also alleviate the complicacy of parking lot management.
The technical solution adopted for the present invention to solve the technical problems is: provide a kind of underground parking intelligent guidance system based on machine learning, comprise central control board, vacancy of parking lots pick-up unit and possess the electronic tag of Voice Navigation and acceleration transducer, described electronic tag is fixed on vehicle interior, described central control board for performing optimal route planning algorithm, dynamic route adjustment algorithm and degree of deep learning algorithm, and with the communication of described electronic tag; Described vacancy of parking lots pick-up unit be used for carrying out communicating with central control board and report parking stall take situation; Described vacancy of parking lots pick-up unit collected parking stall take situation after, be uploaded to described central control board, described central control board finds nearest empty parking space and vehicle is bound by according to entering vehicle, and use optimal path algorithm programme path, after obtaining route, described electronic tag is ordered to carry out Voice Navigation.
Described optimal route planning algorithm navigates for the room entering parking lot vehicle, and using minimal path as primary goal.
Described dynamic route adjustment algorithm is used for when entering many multiple entrances of automobile, prevents the collision between automobile; At route overlapping, described electronic tag can send voice reminder under the guiding of central control board, and there is car in warning car owner front, carries out and dodges preparation; For the vehicle stopped not according to regulation, described dynamic route adjustment algorithm solution will tie up empty parking space and vehicle, and realizes again binding.
Described degree of deep learning algorithm for the Parking situation of the empty parking space distribution situation and car owner that learn parking lot, and gives appropriate parking suggestion.
Described vacancy of parking lots pick-up unit comprises checkout equipment and communication facilities; Described checkout equipment relies on sensor to carry out measuring or adopt camera to carry out image recognition; Described communication facilities is sleep state under normal circumstances, when described central control board receives the request of parking or carries out initialization, sends activation instruction, make communication facilities enter duty to communication facilities, and report parking stall take situation.
Described electronic tag comprises power module, off-line speech utterance module, acceleration sensor module and RFID module; Described power module is used for obtaining electric energy from automotive interior power supply and providing electric energy for electronic tag; Described off-line speech utterance module and described central control board command binding, after receiving the startup voice command that described central control board sends, the voice bound with it by fast searching also make voice answer-back; Described acceleration sensor module is used for the three-dimensional acceleration information of Real-time Obtaining vehicle under plate carries coordinate system, and is calculated the position of vehicle by described central control board; Described RFID module is used for gateway and checks and confirm that neighbouring vehicle will enter parking lot.
At the end of parking navigation, described electronic tag will be connected with car owner's mobile terminal, when connecting disconnection, reports described central control board, and prompting car owner leaves automobile, and described electronic tag proceeds to listen mode automatically; When car owner gets back to parking lot, when described electronic tag detects car owner's mobile terminal within the specific limits, will again connect with car owner's mobile terminal and send vehicle position information, guide car owner to find the vehicle of oneself.
Beneficial effect
Owing to have employed above-mentioned technical scheme, the present invention compared with prior art, has following advantage and good effect: the present invention makes location by inertial data to self, thus realizes reliable inertial navigation fast.The present invention also utilizes central control board to plan and seeks a route, utilize electronic label special guide car owner in order, safety, find parking stall fast, leave parking lot and oppositely seek car.What is more important, over time, become, machine learning algorithm can remind car owner when and where finding parking stall every day, greatly facilitates the orderly management in car owner's parking and parking lot.
Accompanying drawing explanation
Fig. 1 is intelligent guidance system entire block diagram;
Fig. 2 is that automobile enters parking ground navigation schematic diagram;
Scheduling schematic diagram when Fig. 3 is parking navigation.
Embodiment
Below in conjunction with specific embodiment, set forth the present invention further.Should be understood that these embodiments are only not used in for illustration of the present invention to limit the scope of the invention.In addition should be understood that those skilled in the art can make various changes or modifications the present invention, and these equivalent form of values fall within the application's appended claims limited range equally after the content of having read the present invention's instruction.
Embodiments of the present invention relate to a kind of underground parking intelligent guidance system based on machine learning, comprise central control board, vacancy of parking lots pick-up unit and possess the electronic tag of Voice Navigation and acceleration transducer, described electronic tag is fixed on vehicle interior, described central control board for performing optimal route planning algorithm, dynamic route adjustment algorithm and degree of deep learning algorithm, and with the communication of described electronic tag; Described vacancy of parking lots pick-up unit be used for carrying out communicating with central control board and report parking stall take situation; Described vacancy of parking lots pick-up unit collected parking stall take situation after, be uploaded to described central control board, described central control board finds nearest empty parking space and vehicle is bound by according to entering vehicle, and use optimal path algorithm programme path, after obtaining route, described electronic tag is ordered to carry out Voice Navigation.
Described central control board is the computing equipment possessing network connection, the execution of primary responsibility algorithm, the communication with electronic tag.
The execution of described algorithm, comprises optimal route planning algorithm, dynamic route adjustment algorithm (dispatching algorithm), degree of deep learning algorithm, wherein, described optimal route planning algorithm, be mainly used in the room navigation entering parking lot vehicle, preferably, using minimal path as primary goal.Described dynamic route adjustment algorithm (dispatching algorithm), is mainly used in, when entering many multiple entrances of automobile, preventing the collision between automobile.At route overlapping, electronic tag can send voice reminder under the guiding of central control board, and there is car in warning car owner front, carries out and dodges preparation.In addition, the behavior of car owner is freely in itself, and he may stop according to guiding, and when he takies by room that other vehicles are predetermined, the dispatching algorithm of central control board just needs to solve such problem, resets room binding.Described degree of deep learning algorithm, be mainly used in the study empty parking space distribution situation in parking lot and the Parking situation of car owner, and advised with appropriate parking, as certain time of certain day, can have vacant position in some parking lot, even tell the more specific location information in car owner room, to improve parking efficiency, and be convenient to the management in parking lot.The described communication with electronic tag, mainly synchronous the and central control board of label position sends the instruction etc. of playing voice to label.
Described vacancy of parking lots pick-up unit, needs to possess network and connects, can communicate at any time, namely comprise checkout equipment and communication facilities with central control board.Be mainly used in taking situation to central control board report parking stall, wherein, described checkout equipment, can be rely on the measurement carried out of sensor, also can be the image recognition of camera, not limiting device type and using method in the present invention.Described communication facilities, preferably, should using reliability as the condition precedent of equipment choice.In order to reduce the energy ezpenditure of equipment, the equipment moment should do not allowed to be among running, this is also very huge to the pressure of network.General, following trigger mechanism can be added at device interior: in normal sleep state, when central control board receives the request of parking or carries out initialization, send activation instruction to equipment, make equipment enter duty, and report parking space information.
Described electronic tag, low-power consumption, chargeable feature should be had, and possess power module, off-line speech utterance module, acceleration sensor module and RFID module, the interface supported the radio-frequency module of multiple wireless transmission protocol and can be connected with supply unit in car.Electronic tag should be small and exquisite attractive in appearance, and be fixed on automobile driver seat front portion, forbids pulling down.Its flying power should more than at least 2 days.Wherein, described power module, possesses quick-charge function, and directly can obtain electric energy from automotive interior power supply, and maximum current during its work should be limited in below 5mA, ensures low-power consumption and safety.Described off-line speech utterance module, possess the off-line sound bank likely under scene, and with central control board command binding, after receiving the startup voice command that central control board sends, the voice that fast searching is bound with it by off-line voice module also make voice answer-back.Described acceleration sensor module, preferably, recommends the ADXL362 3-axis acceleration sensor of ADI company.This module is used for the three-dimensional acceleration information of Real-time Obtaining automobile under plate carries coordinate system.The position of automobile can be calculated by central control board, and determine how this carries out next step navigation.Described wireless module, need to possess at least two kinds of agreements, protocol type depends on network configuration in parking lot.Agreement is used for the positional information from acceleration transducer data to server transmission and the control command that comprise, and preferably, we recommend bluetooth 4.0 agreement and ICP/IP protocol.Described RFID module, preferably, recommends passive RFID, is mainly used in gateway and checks and confirm that neighbouring automobile will enter parking lot.
The Major Systems block diagram of guidance system provided by the invention as shown in Figure 1.
Native system comprises: central control board 110, direct anchor node 120, parking stall measure equipment 130, electronic tag 140, other parking lots 150, indirect anchor node 160.Wherein:
Central control board 110 is responsible for receiving the signal of parking stall measure equipment 130 and electronic tag 140 by direct anchor node 120 and sending instruction to them; Meanwhile, central control board also by being obtained the parking space information in other parking lots by indirect anchor node 160, for the needs of machine learning.
Direct anchor node 120 not only one, in order to realize the comprehensive covering to parking lot, needs to install multiple node.Namely the effect of direct anchor node 120 is as a signal relay station, is responsible for the information of central control board 110 and parking stall measure equipment 130 and electronic tag 140.In order to the consistance of guarantee information, prevent from colliding to during central control board 110 transmission information at multiple direct anchor node 120, each direct anchor node 120 needs the id information reporting self.
Parking stall measure equipment 130 most of the time is in passive detection state, is also namely in sleep state.Only have just to detect parking stall situation when receiving the order of central control board 110 and report to central control board 110.Every day, stall testing apparatus 130 initiatively can send parking space information, to check the correctness of parking space information to central control board in the time of agreement.
Electronic tag 140 is after formally enabling, and the most of the time is in the sleep state of low-power consumption, is activated when it detects parking lot signal, and sets up with central control board 110 and communicate.After this at inner part of parking lot, electronic tag 140 frequently will communicate with central control board 110 with upper frequency, report the raw data that obtained by acceleration transducer, this information of central control board 110 fast processing also obtains the position of electronic tag 140, and continues to return navigation instruction.Cleverly, electronic tag 140 also can match with the mobile phone of car owner.When this pairing loses connection, electronic tag will proceed to the state of intercepting, when again connecting with car owner's mobile phone, electronic tag 140 will send oneself position to car owner's mobile phone, facilitate car owner to seek car, and get in touch with central control board 110, prepare the navigation of rolling parking lot away from.When sailing out of parking lot, electronic tag 140 can Active report down time, tells parking stall Parking Fee.
As shown in Figure 2, the specific implementation of guidance system will be described by a concrete scene:
First, all car owners need install electronic tag 140 on automobile, and once match with it with mobile phone.After successful matching, electronic tag is formally enabled.(using the initial stage of electronic tag, machine learning is also in learning phase, can not play a role at once.)
Be furnished with numerous direct anchor nodes in field, parking lot, wherein, the both sides near each gateway, have all installed direct anchor node, meanwhile, at the middle position in parking lot, also can install direct anchor node, ensure that whole parking lot is in signal coverage.
When the automobile 1 of electronic tag and automobile 2 being housed when two gateways in parking lot, the broadcast of direct anchor node can will wake electronic tag up from sleep state, then electronic tag is set up with direct anchor node and is communicated, and send acceleration transducer data by direct anchor node to central control board, central control board will check empty parking space situation in parking lot, if empty parking space quantity is more than or equal to automobile number, automobile is then pointed out to enter parking, if empty parking space is less than automobile number, label prompting in Stochastic choice automobile can enter, residue automobile prompting parking stall is not enough, and the parking space information in neighbouring parking lot is provided, simultaneously by parking lot near indirect anchor node notice, automobile may be had to go to this ground.In addition, electronic tag also will send the numbering of the door in automobile 1 and the close parking lot of automobile 2, and record door numbering is the vehicle flowrate in order to add up each, is that machine learning prepares training data.When automobile 1 and automobile 2 sail parking lot into, RFID module is activated, electronic tag sends request navigation command to central control board, central control board runs shortest route-planning algorithm at once, empty parking space 1 and automobile 1 are bound, empty parking space 2 and automobile 2 is bound, calculates path, and return navigation command, start dynamic dispatching simultaneously.After electronic tag receives navigation command, from off-line Voice Navigation storehouse match responding voice, and start Voice Navigation, and communicate with direct anchor node with higher frequency, direct anchor node by information back to central control board.For dynamic dispatching, will be described for Fig. 3 later.
After automobile 1 and automobile 2 arrive appointment empty parking space, central control board first requires the situation of the corresponding empty parking space of parking stall measure equipment inspection and reports to examine whether correctly stop by direct anchor node, then notify that electronic tag stops navigation, and send the voice navigated.Now, electronic tag will be connected with car owner's mobile phone automatically, and when mobile phone signal being detected, electronic tag enters the state of intercepting.To this step, parking navigation completes.
But many times, particularly when we are to a unfamiliar parking lot, such as we remove a strange market shopping, so automobile is parked in the parking lot below market by us.When we done shopping again get back to parking lot time, due to strange landform, we can not find our automobile.As Fig. 2, seeking the car that car car owner wants to find oneself, is also the occupied parking stall in Fig. 2.In the present system, car owner only needs toward central authorities' walking, and after arriving certain location, corresponding electronic tag will listen to the mobile phone of car owner, and then label is by by the positional information of bluetooth to mobile phone transmission automobile, guides car owner to find automobile.Meanwhile, electronic tag can continue to send data to central control board by direct anchor node immediately, and central control board calculates positional information.
After car owner gets on the bus, seek car navigation and terminate, if position starts to change, then central control board will by direct anchor node transmission navigation command, electronic tag after receiving navigation command, for car owner navigates to nearest gateway place.When car owner arrives at gateway, RFID module will be triggered again, electronic tag will send payment request by direct anchor node to central control board, central control board returns after calculating, electronic tag will tell car owner this time Parking Fee after receiving, and returns gateway, parking lot numbering when leaving.Equally, this numbering is used for the training sample of the machine learning algorithm of central control board.Leave parking lot after car owner has paid the fees, when electronic tag cannot detect the signal of direct anchor node again, electronic tag enters sleep state automatically.
Here special it is to be understood that, when central control board utilizes machine learning algorithm after have learned a period of time, when car owner leaves parking lot each time, the probability which of car owner tomorrow being reminded in which parking lot to have vacant position by electronic tag warmth comparatively large the time, and again send prompting after upper once motor racing.When car owner is near parking lot each time, also car owner can be reminded which selection of time to leave parking lot in by electronic tag warmth and can avoid wagon flow peak.
Next, by composition graphs 3, provide illustrating of dynamic dispatching:
As shown in Figure 3, automobile 1 and automobile 2 prepare to enter parking lot from gateway A and gateway E respectively, and are bound with parking stall 1 and parking stall 2 respectively by central control board.Wherein, be labeled with ◎ and represent empty parking space, other are for account for parking stall.Obviously, automobile 1 and automobile 2 all have to pass through flex point G and could arrive empty parking space.So, when two cars arrives flex point G all soon, if do not add guiding, the possibility of collision is had.Now, central control board will be random to a car, such as automobile 2, sends collision warning and require to stop, wait automobile 1 cross after stop warning.Automobile can be avoided like this to seek the collision in a process, cause in parking lot crowded and chaotic.
Now, according to binding rule and navigation instruction, automobile 1 should be parked in the position of parking stall 1.But car owner might not be stopped according to the instruction of navigation, and from traditionally, car owner is more prone to parkings of keeping right, so automobile 1 is run counter to navigation and is indicated, and has been parked in the position of parking stall 2.When central control board finds that automobile 1 departs from navigation instruction, will by the voice command of direct anchor node to electronic tag mistake in communication route, then electronic tag will point out car owner to depart from projected route.When finding that car owner has been parked on a parking stall of being bound by automobile 2, first central control board command facility pick-up unit checks room situation and reports, after examining, remove the binding of this room and automobile 2 at once, and the room of automobile 1 correspondence and automobile 2 are bound, upgraded the navigation information of electronic tag simultaneously by direct anchor node, complete the binding again of parking stall.
Claims (7)
1. the underground parking intelligent guidance system based on machine learning, comprise central control board, vacancy of parking lots pick-up unit and possess the electronic tag of Voice Navigation and acceleration transducer, described electronic tag is fixed on vehicle interior, it is characterized in that, described central control board for performing optimal route planning algorithm, dynamic route adjustment algorithm and degree of deep learning algorithm, and with the communication of described electronic tag; Described vacancy of parking lots pick-up unit be used for carrying out communicating with central control board and report parking stall take situation; Described vacancy of parking lots pick-up unit collected parking stall take situation after, be uploaded to described central control board, described central control board finds nearest empty parking space and vehicle is bound by according to entering vehicle, and use optimal path algorithm programme path, after obtaining route, described electronic tag is ordered to carry out Voice Navigation.
2. the underground parking intelligent guidance system based on machine learning according to claim 1, is characterized in that, described optimal route planning algorithm navigates for the room entering parking lot vehicle, and using minimal path as primary goal.
3. the underground parking intelligent guidance system based on machine learning according to claim 1, is characterized in that, described dynamic route adjustment algorithm is used for when entering many multiple entrances of automobile, prevents the collision between automobile; At route overlapping, described electronic tag can send voice reminder under the guiding of central control board, and there is car in warning car owner front, carries out and dodges preparation; For the vehicle stopped not according to regulation, described dynamic route adjustment algorithm solution will tie up empty parking space and vehicle, and realizes again binding.
4. the underground parking intelligent guidance system based on machine learning according to claim 1, is characterized in that, described degree of deep learning algorithm for the Parking situation of the empty parking space distribution situation and car owner that learn parking lot, and gives appropriate parking suggestion.
5. the underground parking intelligent guidance system based on machine learning according to claim 1, is characterized in that, described vacancy of parking lots pick-up unit comprises checkout equipment and communication facilities; Described checkout equipment relies on sensor to carry out measuring or adopt camera to carry out image recognition; Described communication facilities is sleep state under normal circumstances, when described central control board receives the request of parking or carries out initialization, sends activation instruction, make communication facilities enter duty to communication facilities, and report parking stall take situation.
6. the underground parking intelligent guidance system based on machine learning according to claim 1, is characterized in that, described electronic tag comprises power module, off-line speech utterance module, acceleration sensor module and RFID module; Described power module is used for obtaining electric energy from automotive interior power supply and providing electric energy for electronic tag; Described off-line speech utterance module and described central control board command binding, after receiving the startup voice command that described central control board sends, the voice bound with it by fast searching also make voice answer-back; Described acceleration sensor module is used for the three-dimensional acceleration information of Real-time Obtaining vehicle under plate carries coordinate system, and is calculated the position of vehicle by described central control board; Described RFID module is used for gateway and checks and confirm that neighbouring vehicle will enter parking lot.
7. the underground parking intelligent guidance system based on machine learning according to claim 1, it is characterized in that, at the end of parking navigation, described electronic tag will be connected with car owner's mobile terminal, when connecting disconnection, report described central control board, prompting car owner leaves automobile, and described electronic tag proceeds to listen mode automatically; When car owner gets back to parking lot, when described electronic tag detects car owner's mobile terminal within the specific limits, will again connect with car owner's mobile terminal and send vehicle position information, guide car owner to find the vehicle of oneself.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003151088A (en) * | 2001-11-16 | 2003-05-23 | Nippon Telegraph & Telephone East Corp | Management terminal for mobile stop position management system, mobile stop position management system, mobile stop position management method, program for management terminal in mobile stop position management system and recording medium for management terminal in mobile stop position management system |
| CN101789188A (en) * | 2010-03-24 | 2010-07-28 | 天津信电科技发展有限公司 | Dynamic parking navigation system used at inner part of parking lot |
| CN101946269A (en) * | 2008-02-14 | 2011-01-12 | 丰田自动车株式会社 | Parking lot guidance system |
| CN102930740A (en) * | 2011-08-11 | 2013-02-13 | 大同大学 | Parking lot vacancy navigation device |
| CN202827481U (en) * | 2012-08-20 | 2013-03-27 | 东莞能创信息科技有限公司 | Vehicle anti-theft system for seeking and locating |
| CN103050022A (en) * | 2012-12-26 | 2013-04-17 | 南京丰泰通信技术股份有限公司 | Method and system for carrying out multimedia navigation on parking in parking lot |
| CN103258438A (en) * | 2013-04-25 | 2013-08-21 | 山东省科学院自动化研究所 | Intelligent going-out and best carport navigation system and navigation method thereof |
| JP2014189265A (en) * | 2013-03-28 | 2014-10-06 | Toyota Motor Corp | Parking support device |
| CN104112370A (en) * | 2014-07-30 | 2014-10-22 | 哈尔滨工业大学深圳研究生院 | Monitoring image based intelligent parking lot parking place identification method and system |
| CN104391966A (en) * | 2014-12-03 | 2015-03-04 | 中国人民解放军国防科学技术大学 | Typical car logo searching method based on deep learning |
| JP5692628B2 (en) * | 2010-02-26 | 2015-04-01 | アイシン・エィ・ダブリュ株式会社 | Navigation device and navigation program |
| CN104697517A (en) * | 2015-03-26 | 2015-06-10 | 江南大学 | Multi-target tracking and positioning system for indoor parking lot |
| CN104755343A (en) * | 2012-10-29 | 2015-07-01 | 奥迪股份公司 | Method for coordinating the operation of motor vehicles that drive in fully automated mode |
| CN104821097A (en) * | 2015-05-22 | 2015-08-05 | 蒋君伟 | Indoor parking lot positioning vehicle-searching method and system |
| JP6160509B2 (en) * | 2014-02-25 | 2017-07-12 | アイシン・エィ・ダブリュ株式会社 | Driving support device, driving support method and program |
-
2015
- 2015-10-16 CN CN201510672225.6A patent/CN105390018B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003151088A (en) * | 2001-11-16 | 2003-05-23 | Nippon Telegraph & Telephone East Corp | Management terminal for mobile stop position management system, mobile stop position management system, mobile stop position management method, program for management terminal in mobile stop position management system and recording medium for management terminal in mobile stop position management system |
| CN101946269A (en) * | 2008-02-14 | 2011-01-12 | 丰田自动车株式会社 | Parking lot guidance system |
| JP5692628B2 (en) * | 2010-02-26 | 2015-04-01 | アイシン・エィ・ダブリュ株式会社 | Navigation device and navigation program |
| CN101789188A (en) * | 2010-03-24 | 2010-07-28 | 天津信电科技发展有限公司 | Dynamic parking navigation system used at inner part of parking lot |
| CN102930740A (en) * | 2011-08-11 | 2013-02-13 | 大同大学 | Parking lot vacancy navigation device |
| CN202827481U (en) * | 2012-08-20 | 2013-03-27 | 东莞能创信息科技有限公司 | Vehicle anti-theft system for seeking and locating |
| CN104755343A (en) * | 2012-10-29 | 2015-07-01 | 奥迪股份公司 | Method for coordinating the operation of motor vehicles that drive in fully automated mode |
| CN103050022A (en) * | 2012-12-26 | 2013-04-17 | 南京丰泰通信技术股份有限公司 | Method and system for carrying out multimedia navigation on parking in parking lot |
| JP2014189265A (en) * | 2013-03-28 | 2014-10-06 | Toyota Motor Corp | Parking support device |
| CN103258438A (en) * | 2013-04-25 | 2013-08-21 | 山东省科学院自动化研究所 | Intelligent going-out and best carport navigation system and navigation method thereof |
| JP6160509B2 (en) * | 2014-02-25 | 2017-07-12 | アイシン・エィ・ダブリュ株式会社 | Driving support device, driving support method and program |
| CN104112370A (en) * | 2014-07-30 | 2014-10-22 | 哈尔滨工业大学深圳研究生院 | Monitoring image based intelligent parking lot parking place identification method and system |
| CN104391966A (en) * | 2014-12-03 | 2015-03-04 | 中国人民解放军国防科学技术大学 | Typical car logo searching method based on deep learning |
| CN104697517A (en) * | 2015-03-26 | 2015-06-10 | 江南大学 | Multi-target tracking and positioning system for indoor parking lot |
| CN104821097A (en) * | 2015-05-22 | 2015-08-05 | 蒋君伟 | Indoor parking lot positioning vehicle-searching method and system |
Cited By (25)
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|---|---|---|---|---|
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| CN105931490A (en) * | 2016-06-27 | 2016-09-07 | 合肥指南针电子科技有限责任公司 | Automatic parking space navigation system |
| CN106157686A (en) * | 2016-07-28 | 2016-11-23 | 苏州市享乐惠信息科技有限公司 | A kind of automatic stopping inducible system |
| CN106548645A (en) * | 2016-11-03 | 2017-03-29 | 济南博图信息技术有限公司 | Vehicle route optimization method and system based on deep learning |
| CN108257407A (en) * | 2016-12-28 | 2018-07-06 | 电信科学技术研究院 | A kind of vehicle parking navigation and processing method, the device of navigation information |
| CN106940934A (en) * | 2017-01-16 | 2017-07-11 | 斑马信息科技有限公司 | Intelligent vehicle-tracing system and its application |
| CN107862895A (en) * | 2017-10-31 | 2018-03-30 | 西安科锐盛创新科技有限公司 | Underground parking navigation system |
| CN107564332A (en) * | 2017-10-31 | 2018-01-09 | 奇瑞汽车股份有限公司 | Stop position prompt method, system, computer-readable recording medium |
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| CN109841082B (en) * | 2017-11-28 | 2022-12-02 | 丰田自动车株式会社 | Notification system, notification device, and notification method |
| US11335198B2 (en) | 2017-11-28 | 2022-05-17 | Toyota Jidosha Kabushiki Kaisha | Notification system, notification device, and notification method |
| CN108151752A (en) * | 2017-11-30 | 2018-06-12 | 重庆元铂智能科技有限公司 | A kind of air navigation aid based on car-mounted terminal |
| CN110189535A (en) * | 2018-02-22 | 2019-08-30 | 方文淋 | A kind of specified parking stall voice guide system in underground parking |
| WO2019178992A1 (en) * | 2018-03-20 | 2019-09-26 | 深圳市方格尔科技有限公司 | Vehicle-mounted parking billing apparatus, and parking billing system and method |
| CN108416854A (en) * | 2018-03-20 | 2018-08-17 | 深圳市方格尔科技有限公司 | A kind of vehicle-mounted parking charge device, parking charge system and method |
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| CN112272635B (en) * | 2018-06-08 | 2023-08-11 | 罗伯特·博世有限公司 | Method and vehicle system for optimizing parking advice |
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| US20210372812A1 (en) * | 2018-06-08 | 2021-12-02 | Robert Bosch Gmbh | Method and vehicle system for optimizing parking suggestions |
| CN109584611A (en) * | 2018-12-17 | 2019-04-05 | 山东建筑大学 | Parking lot free time parking stall judgment means and method based on ERI and network |
| CN109754614A (en) * | 2019-01-29 | 2019-05-14 | 成都信息工程大学 | A kind of parking navigation system, method and computer readable storage medium |
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