CN104236566A - Map matching method based on intelligent mobile phone - Google Patents
Map matching method based on intelligent mobile phone Download PDFInfo
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- CN104236566A CN104236566A CN201410495525.7A CN201410495525A CN104236566A CN 104236566 A CN104236566 A CN 104236566A CN 201410495525 A CN201410495525 A CN 201410495525A CN 104236566 A CN104236566 A CN 104236566A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
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Abstract
The invention discloses a map matching method based on an intelligent mobile phone. The map matching method comprises the steps of installing an APP with the data acquisition function on the intelligent mobile phone; fixing the intelligent mobile phone in a vehicle, and starting the APP with the data acquisition function; driving the vehicle to run on a straight road and curves and manually marking events to acquire data of an acceleration sensor; obtaining a coordinate system of the intelligent mobile phone and a coordinate system of the vehicle; correcting the acquired data of the acceleration sensor; performing training classification on the marked and corrected data of the acceleration sensor to obtain a road judgment model; collecting actually-measured road condition data, judging the type of the road according to the road judgment model, and realizing map matching according to road topological information. According to the map matching method based on the intelligent mobile phone, the curves are detected through a mobile phone sensor, and an existing navigation system is corrected, so that the precision of a civil GPS (global positioning system) and the inaccuracy of a map system can be compensated to a certain extent, a more accurate navigation service can be provided, and a driving behavior is safer.
Description
Technical field
The present invention relates to numerical map and map matching technology field, particularly relate to a kind of map-matching method based on smart mobile phone.
Background technology
Along with economic development, road traffic is day by day flourishing, and the trip of people becomes more convenient, and traditional map becomes history already, and GPS (Global Positioning System, GPS) is auxiliary for people's trip now provides navigation.The transmission of the position signalling of the receiving end that gps system utilizes multi-satellite to transmit and correction realize the location, position of receiving end, and the constellation systems of 24 satellite formations almost can be covering the whole world.And our common onboard system is exactly gps system and Geographic Information System (Geographic Information System now, GIS) combination, utilizes the cartographic information of the site of road in electronic chart to compensate civilian GPS positioning precision problem on the low side to a certain extent.With the lifting of wireless communication technology, GPS receiving end can narrow down to a sensor size and be encapsulated in mobile phone, therefore the pattern of intelligent mobile phone platform lift-launch navigational system is also day by day universal, and expedite the emergence of out the application of a large amount of advanced drive assist system, they can provide more comfortable, better efficient, safer service.The function of traditional vehicular map can have been replaced now with mobile phone.
But when complex road condition, no matter be traditional vehicular map or Mobile Telephone Gps, all exist and locate situation that is inaccurate, the geographical loss of learning of part, especially tunnel crossed by vehicle, in the situations such as bend, this deficiency is more obvious.Therefore this patent proposes a kind of based on intelligent mobile phone platform, and the correction vehicle mounted guidance of vehicle bend state position information drives aided positioning system to utilize mobile phone sensor to judge.
Smart mobile phone fast development, world market research company (Gartner) data show, the second quarter in 2013 whole world smart mobile phone sales volume first excursion functional mobile phone.Investigated display mobile phone operating system Android (Android) system global market share of increasing income second quarter last year is 79% simultaneously, defines the situation of dominance.People so one of reason growing tender of smart mobile phone are exactly, and conventional mobile phone compares, the built-in many simple and convenient application program of mobile phone (Application, APP) of smart mobile phone, can provide more comfortable, adventure in daily life easily to people.And these APP much have benefited from the inner integrated all multisensors of smart mobile phone.Generally, smart mobile phone inside can the sensor device etc. such as integrated temperature sensor, gravity sensing, range sensor, electronic compass, light sensor, three-axis gyroscope, infrared ray sensor, and this provides a powerful and platform easily just to follow-up APP exploitation.
Gps system originates from the U.S. in the fifties latter stage in last century, and coming into operation with the sixties, is for military use designs at first, after open civil area gradually, but positioning precision is far below military.Whole system forms constellation by 24 satellites, the region of orientation range covering the whole world 98%.In general, completing conventional navigation feature needs Navsat and ground receiving equipment, and Navsat ceaselessly sends the position of time and satellite, and interval circulation in 30 seconds is launched; The message sent by the receiver reception Navsat on ground, measure the distance between known location satellite and receiver, revised by the data of comprehensive multi-satellite, general makeover process at least needs the information of 4 satellites.General vehicular map utilizes gps system to navigate just.But for open civilian code, due to reasons such as satellite clock correction, satellite ephemeris error, ionosphere delay error, tropospheric delay error, multipath effect error, receiver noise error, shelters, precision can only reach about about 20m; When the accuracy of map is higher, navigation accuracy can improve accordingly.But at bend, the bend situation especially under the dangerous road conditions such as winding road, the positioning precision of navigation is still undesirable, and this just needs correction in conjunction with technology such as bend detections to promote precision.
By the method utilizing software to rectify a deviation, registration correction is carried out to the locator data of vehicle and electronic map data in prior art " map-matching algorithm based on topological structure is studied ", reduce the display error between the road information of electronic chart and GPS locating information.Calculate road weight to be matched, adopt fuzzy matching strategy, remove and be not communicated with road and finally adopt linear interpolation method to carry out map match.Be not less than 96% by the coupling accuracy testing this matching algorithm, single-point is no more than 10ms match time.The method has been applied to actual engineer applied, and achieves good effect.
Weights are utilized to select coupling section in prior art " map-matching algorithm in GPS navigation system ", and GPS (GPS) tracing point is projected on coupling section, the error of track of vehicle point along road direction is eliminated with parallelogram matching criterior in intersection, upgraded by dynamic deviation, solution satellite changes the offset issue that the factors such as star, air cloud cover, multipath effect cause.The GPS onboard system introducing this algorithm in Hefei City on the spot sport car experimental result show, it still can correctly mate for complex road condition, true reappearance vehicle travel situations.
Therefore prior art majority adopts map match to carry out error correction to navigational system, promotes precision.Map match mainly solves: find the road of vehicle current driving and projected to by current anchor point on the road of vehicle traveling.Although existing map-matching algorithm coupling accuracy is higher, but still has the following disadvantages:
(1) the selected of travel can affect algorithm performance, and the performance for different sections of highway algorithm exists notable difference;
(2) consuming time more, due to reasons such as GPS error, in the process finding coupling road, there is larger time loss;
(3) GPS energy consumption is high, and at 1 ~ 50hz not etc., therefore real-time performance advantage is not obvious for GPS renewal speed;
(4) in bend situation, map match difficulty is obvious, if the speed of a motor vehicle is 1hz from 50 ~ 100km/s, GPS renewal speed, so, the possible gap 13.9 ~ 27.8m of GPS point is not etc., this is if in vehicle turning place or the section that changes greatly, and this difficulty of matching is just more remarkable.
Current method has employing video correction also to have the algorithm for pattern recognition adopted based on topological structure, but owing to needing extra collecting device and computing, this correction easily portability is lower, and cost budgeting is higher.And in general, in straight way and non-complex section, current GPS positioning precision can meet navigation needs substantially, need the urgent section of bend often and the complex road condition that promote its accuracy requirement.
Therefore, for above-mentioned technical matters, be necessary to provide a kind of map-matching method based on smart mobile phone.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of map-matching method based on smart mobile phone, it uses the daily smart mobile phone used of people as platform tools, by detecting the motion state of vehicle, detect vehicle and whether be in bend place, in conjunction with existing navigational system, its positioning error is revised, navigation accuracy promotes.
To achieve these goals, the technical scheme that provides of the embodiment of the present invention is as follows:
Based on a map-matching method for smart mobile phone, said method comprising the steps of:
S1, smart mobile phone is installed there is the APP of data acquisition function;
S2, smart mobile phone is fixed in vehicle, opens the APP with data acquisition function;
S3, steering vehicle travel forthright and bend, and manually carry out event mark, obtain acceleration transducer data;
The corresponding relation of S4, acquisition smart mobile phone coordinate system and vehicle axis system;
S5, to obtain acceleration transducer data correct;
S6, the acceleration transducer data marking and correct are carried out to training classification, obtain road discrimination model;
S7, collection actual measurement road condition data, judge category of roads according to road discrimination model, and in conjunction with road topology information realization map match.
As a further improvement on the present invention, the data that the APP in described smart mobile phone gathers comprise gps data and acceleration transducer data.
As a further improvement on the present invention, described acceleration transducer data comprise vehicle and travel tangential direction, horizontal tangent direction and perpendicular to the linear acceleration on surface level upward direction three directions.
As a further improvement on the present invention, described gps data comprises longitude, latitude, sea level elevation.
As a further improvement on the present invention, described gps data and acceleration transducer data also comprise cell phone system time and mobile phone to time of data acquisition.
As a further improvement on the present invention, described step S6 is specially:
Adopt supervised learning method, training acceleration transducer data also form road discrimination model, after model has been set up, and the accuracy of test data inspection road discrimination model and robustness.
As a further improvement on the present invention, described step S6 comprises:
S61, data prediction, go dirty and denoising to data;
S62, feature extraction, extract temporal signatures and the frequency domain character of data;
S63, employing supervised learning method establishment road discrimination model.
As a further improvement on the present invention, described temporal signatures comprises the average of accekeration in pretreated accekeration, each segment and variance, the average of whole segment data and variance; Frequency domain character comprises pretreated accekeration and is transformed into the range value of frequency domain, the average of range value and variance.
As a further improvement on the present invention, the category of roads of described road discrimination model comprise obvious, the right bend of left bend obviously, not obvious, the right bend of forthright, left bend is not obvious.
The present invention has following beneficial effect:
The present invention utilizes mobile phone sensor to carry out bend detection and carries out revising to existing navigational system and can compensate the precision of civilian gps system and the inaccurate of map system to a certain extent, especially the bend section that takes place frequently of turning behavior and the driving behavior complicated highway section compared with horn of plenty is being driven, this kind of compensation can provide navigation Service more accurately, makes driving behavior safer.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, the accompanying drawing that the following describes is only some embodiments recorded in the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the particular flow sheet of the map-matching method that the present invention is based on smart mobile phone.
Fig. 2 a, 2b are respectively the coordinate schematic diagram of smart mobile phone and vehicle in the embodiment of the invention.
Fig. 3 is the original acceleration sensor values schematic diagram comprising misdata and noise data in the embodiment of the invention.
Fig. 4 removes the acceleration transducer value schematic diagram after dirty and denoising through data prediction in the embodiment of the invention.
Fig. 5 is the template schematic diagram of sorting technique in the embodiment of the invention.
Embodiment
Technical scheme in the present invention is understood better in order to make those skilled in the art person, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, should belong to the scope of protection of the invention.
The present invention be based on intelligent mobile phone platform bend detect so that carry out navigation map modification method.The intelligent mobile phone sensor adopted is the built-in acceleration transducer of interior of mobile phone and GPS receiver module.First by the data analysis that acceleration transducer gathers, judge whether vehicle runs over the information such as bend and bend situation.Next by conjunction with road topology information, the coupling of numerical map and vehicle place road (bend or straight way) is realized, to revising the disappearance of GPS positioning error and cartographic information.
Shown in ginseng Fig. 1, the invention discloses a kind of map-matching method based on smart mobile phone, comprise the following steps:
One, travel differentiates:
S1, smart mobile phone is installed there is the APP of data acquisition function;
S2, smart mobile phone is fixed in vehicle, opens the APP with data acquisition function;
S3, steering vehicle travel forthright and bend, and manually carry out event mark, obtain acceleration transducer data;
The corresponding relation of S4, acquisition smart mobile phone coordinate system and vehicle axis system;
S5, to obtain acceleration transducer data correct;
S6, the acceleration transducer data marking and correct are carried out to training classification, obtain road discrimination model.Step S6 specifically comprises:
S61, data prediction, go dirty and denoising to data;
S62, feature extraction, extract temporal signatures and the frequency domain character of data;
S63, employing supervised learning method establishment road discrimination model.
Two, bend topology information coupling and vehicular map correction:
S7, collection actual measurement road condition data, judge category of roads according to road discrimination model, and in conjunction with road topology information realization map match.
Below in conjunction with embodiment, the invention will be further described.
One, travel type judges
S1, the APP of data acquisition function will be had to install on smart mobile phone.
Data acquisition A PP is write based on intelligent mobile phone platform, mainly contain data acquisition and start button, and collection stops key, and show road type: obviously bend starts, obviously bend terminates, not obvious bend starts, not obvious bend terminates, forthright starts and forthright terminates.
The data gathered comprise gps data and acceleration transducer data, and gps data form is as table 2, and acceleration transducer data are as table 1.Wherein acceleration is the key next judging vehicle-state, and it utilizes principle of inertia, can perceive the change of accelerating force.Accelerating force is exactly when object acts on power on object in accelerator, such as rocks, falls, rises, the various mobile change of lower degradation can be converted into electric signal by it.Then comprise location in gps signal, test the speed and high-precision time standard.The data that we collect be longitude in GPS, latitude, sea level elevation, cell phone system time and mobile phone to time of data acquisition.
Table 1 acceleration transducer data and time
Table 2 gps data and time
S2, smart mobile phone are fixed in vehicle, and turn-on data capture program.
A, mobile phone are placed onboard by operator seat, and use stationary installation to be fixed, in order to easy process use double sticky tape by fixing for mobile phone onboard, ensure that mobile phone and vehicle relative displacement do not occur, in motion process, do same three-dimensional space motion;
B, the built-in three axle gravitational accelerometer of use smart mobile phone measuring vehicle can travel tangential direction, horizontal tangent direction and the linear acceleration perpendicular to surface level upward direction.
In present embodiment, three axle gravitational accelerometer institute values can be obtained through difference by three axle velographs, also can carry out second order difference by three-shaft displacement meter and obtain.
Wherein, the three-dimensional system of coordinate of vehicle as shown in Figure 2 b.Driver's seat and front passenger's seat line the direction pointing to front passenger's seat is set to X-axis positive dirction, along car body and the direction pointing to headstock is Y-axis positive dirction, what point into the sky perpendicular to chassis and by X-axis and Y-axis intersection point and direction is Z axis positive dirction.The three-dimensional system of coordinate of mobile phone is if Fig. 2 a is along mobile phone long side direction, and point to receiver direction for Y direction, on the table that mobile phone is placed, one end of microphone is near the health of people, along mobile phone short side direction, and the direction pointed on the right side of people is X-axis positive dirction, perpendicular to mobile phone screen and the direction pointed into the sky is Z axis positive dirction.
S3, steering vehicle travel forthright and bend, and manually carry out event mark, obtain acceleration transducer data.
Drive under different road conditions, gather road data with APP in smart mobile phone, by the situation of road as forthright, bend is obvious and bend is not obvious marks.Particularly, in present embodiment, marking types comprises: obvious, the right bend of left bend is obvious, not obvious, the right bend of forthright, left bend is not obvious.
S4, obtained the corresponding relation of smart mobile phone coordinate system and vehicle axis system by easily-testing.
Present embodiment with reference to one section of article " Sensing vihicle dynamics for determining driver phoneuse " of MOBISYS2013, and simultaneously the method that author uses in this section of article applies acceleration transducer and gyro sensor.Concrete steps are as follows:
When a, stationary vehicle, mobile phone can detect acceleration situation now and record on vehicle.The 3-axis acceleration vector value obtained this time is just equivalent to the acceleration of gravity of vehicle by the earth.The vector acceleration normalization obtained is obtained column vector
B, startup vehicle, not adjustment direction dish, ensure that automobile linearly moves ahead.Record automobile is to the process of preacceleration, and be normalized the vector acceleration obtained, this step is equivalent to the vector of unit length along Y-axis positive dirction obtained in automobile coordinate, is denoted as column vector
C, according to right-hand rule, the unit column vector of X-axis positive dirction can be obtained
the transition matrix be made up of these three vectors is:
The data right side obtained from acceleration transducer is taken advantage of
obtain the motion state in required real three-dimensional vehicle space.
S5, to obtain acceleration transducer data correct, application S4 in bearing calibration the sensing data obtained in S3 is corrected.
S6, training classification is carried out to the acceleration transducer data marking and correct obtain road discrimination model.
Adopt supervised learning method, training data formation model.Model has been set up, test data, the accuracy of testing model and robustness.
Specifically be divided into following step:
S61, data prediction:
Separability is had more in order to make model, need before training to carry out preprocessing process, specifying information disposal route: information processing is from raw data place, carry out coordinates correction, noise eliminating, removal misdata, extraction validity feature information, category of roads classification, the extraction of numerical map road information, path adaptation etc.
Data prediction mainly realizes two functions: the coordinates correction of data and filtering noise, dirty data etc.The data obtained from mobile phone sensor are the data that there are noise and some random errors, keep flat on surface level, also can produce the data of noise and random error even if mobile phone is static.
For the precision of raising system of trying one's best, reduce the erroneous judgement that misdata is brought, these data need noise decrease, filter misdata.The measure that the present invention takes first removes misdata, then adopt smothing filtering, reduces the noise of data.Shared by misdata, the number percent of overall data is not high, first the data obtained can be sorted, (in present embodiment, M is set as 10 to M value of removal maximum absolute value in specific experiment, because each data length intercepted is 150 data, the sample frequency of acceleration transducer is set to 50hz, weed out 10 data in so every 150 data and in fact can't judge that effect causes large impact to data itself, but misdata can be filtered, avoid erroneous judgement).After this, the effect of dirty data is gone in the denoising that the value filtering that the data obtained is averaged just can reach expectation.
The effect of the rear front and back of process can be contrasted, as shown in Figure 3 and Figure 4.Packet before treatment is containing noisy noise and some manifest error data.Data noise after process obviously reduces, and misdata is removed clean, and does not have a significant impact the information of data itself.
S62, feature extraction:
The feature extracted comprises temporal signatures and frequency domain character.
Temporal signatures comprises: pretreated accekeration, the average of the average of each segment (can be set to 10 ~ 20 values is a segment) inner accekeration and variance, whole segment data and variance.Frequency domain character comprises: pretreated accekeration is transformed into the range value of frequency domain, the average of range value and variance.
Then, by selecting signature template determination road conditions: bend is obvious, forthright, bend are not obvious.In actual template is selected, bend is divided into again turning left and turns right, and the unconspicuous situation of bend is also divided into turning left and turns right.Be actually a bend classification in the present invention to classify as: obvious, the right bend of left bend is obvious, not obvious, the right bend of forthright, left bend this five class not obvious.
S63, supervised learning method establishment disaggregated model.
The sorting technique that present embodiment adopts, without dynamic time consolidation, uses the template in Fig. 5.In Fig. 4, data are in the repeatedly driving procedure of exper ienced driver on forthright and each bend and gather, and reject misdata.
Two, bend topology information coupling and vehicular map correction
After choosing template, can by the acceleration transducer data determination road conditions collected: bend is obviously, forthright, bend be not obvious, then Water demand numerical map road topology situation.
Road information is made up of a series of GPS point, comprises longitude data and latitude data in GPS information, according to the information of GPS, longitude and latitude is regarded as two information, by curve, forms the related function of longitude and latitude.Then on GPS point, asking slope respectively by the curve after matching, namely the tangent slope of curve on GPS point, after arc tangent, is namely tangent angle.
Concrete matlab code is as follows:
So, just can obtain the geological information on road, also just easily via judging that angle is to know the road information such as specifically obvious, the right bend of left bend is obvious, the not obvious and right bend of forthright, left bend is not obvious.Just the information of numerical map can be insinuated in the dimension comprising path connected information and road curve information in conjunction with information such as map path connecteds.
Then the bend information etc. the bend information analyzing out from acceleration transducer that smart mobile phone obtains and map is mated.If the information after map conversion is identical with the information that sensor judges, so just by Current GPS Point matching on current road segment, otherwise re-start again and detect and search etc.
In sum, the present invention utilizes mobile phone sensor to carry out bend detection and carries out revising to existing navigational system and can compensate the precision of civilian gps system and the inaccurate of map system to a certain extent, especially the bend section that takes place frequently of turning behavior and the driving behavior complicated highway section compared with horn of plenty is being driven, this kind of compensation can provide navigation Service more accurately, makes driving behavior safer.
To those skilled in the art, obviously the invention is not restricted to the details of above-mentioned one exemplary embodiment, and when not deviating from spirit of the present invention or essential characteristic, the present invention can be realized in other specific forms.Therefore, no matter from which point, all should embodiment be regarded as exemplary, and be nonrestrictive, scope of the present invention is limited by claims instead of above-mentioned explanation, and all changes be therefore intended in the implication of the equivalency by dropping on claim and scope are included in the present invention.Any Reference numeral in claim should be considered as the claim involved by limiting.
In addition, be to be understood that, although this instructions is described according to embodiment, but not each embodiment only comprises an independently technical scheme, this narrating mode of instructions is only for clarity sake, those skilled in the art should by instructions integrally, and the technical scheme in each embodiment also through appropriately combined, can form other embodiments that it will be appreciated by those skilled in the art that.
Claims (9)
1. based on a map-matching method for smart mobile phone, it is characterized in that, said method comprising the steps of:
S1, smart mobile phone is installed there is the APP of data acquisition function;
S2, smart mobile phone is fixed in vehicle, opens the APP with data acquisition function;
S3, steering vehicle travel forthright and bend, and manually carry out event mark, obtain acceleration transducer data;
The corresponding relation of S4, acquisition smart mobile phone coordinate system and vehicle axis system;
S5, to obtain acceleration transducer data correct;
S6, the acceleration transducer data marking and correct are carried out to training classification, obtain road discrimination model;
S7, collection actual measurement road condition data, judge category of roads according to road discrimination model, and in conjunction with road topology information realization map match.
2. method according to claim 1, is characterized in that, the data that the APP in described smart mobile phone gathers comprise gps data and acceleration transducer data.
3. method according to claim 2, is characterized in that, described acceleration transducer data comprise vehicle and travel tangential direction, horizontal tangent direction and perpendicular to the linear acceleration on surface level upward direction three directions.
4. method according to claim 2, is characterized in that, described gps data comprises longitude, latitude, sea level elevation.
5. method according to claim 2, is characterized in that, described gps data and acceleration transducer data also comprise cell phone system time and mobile phone to time of data acquisition.
6. method according to claim 1, is characterized in that, described step S6 is specially:
Adopt supervised learning method, training acceleration transducer data also form road discrimination model, after model has been set up, and the accuracy of test data inspection road discrimination model and robustness.
7. method according to claim 1, is characterized in that, described step S6 comprises:
S61, data prediction, go dirty and denoising to data;
S62, feature extraction, extract temporal signatures and the frequency domain character of data;
S63, employing supervised learning method establishment road discrimination model.
8. method according to claim 7, is characterized in that, described temporal signatures comprises the average of accekeration in pretreated accekeration, each segment and variance, the average of whole segment data and variance; Frequency domain character comprises pretreated accekeration and is transformed into the range value of frequency domain, the average of range value and variance.
9. method according to claim 7, is characterized in that, the category of roads of described road discrimination model comprise obvious, the right bend of left bend obviously, not obvious, the right bend of forthright, left bend is not obvious.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104615528A (en) * | 2015-02-09 | 2015-05-13 | 兰州理工大学 | Intelligent cell phone sensor data online collecting and real-time processing method |
| CN105890600A (en) * | 2016-04-14 | 2016-08-24 | 南京大学 | Subway passenger position inferring method based on mobile phone sensors |
| CN106355927A (en) * | 2016-08-30 | 2017-01-25 | 成都路行通信息技术有限公司 | GPS (global positioning system) mark point determining method as well as GPS trajectory optimization method and device |
| CN106530696A (en) * | 2016-11-17 | 2017-03-22 | 捷开通讯(深圳)有限公司 | Driving behavior monitoring method and road condition monitoring method |
| CN107990909A (en) * | 2016-10-27 | 2018-05-04 | 千寻位置网络有限公司 | A kind of test method and its system of simulated roadway position data |
| CN108106620A (en) * | 2017-12-20 | 2018-06-01 | 中国科学院深圳先进技术研究院 | A kind of topology road matching method, system and electronic equipment |
| US10060751B1 (en) | 2017-05-17 | 2018-08-28 | Here Global B.V. | Method and apparatus for providing a machine learning approach for a point-based map matcher |
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| CN112945230A (en) * | 2021-01-26 | 2021-06-11 | 腾讯科技（深圳）有限公司 | Vehicle driving state identification method and device, computer equipment and storage medium |
| CN113514072A (en) * | 2021-09-14 | 2021-10-19 | 自然资源部第三地理信息制图院 | Road matching method oriented to navigation data and large-scale drawing data |
| CN114387777A (en) * | 2020-10-20 | 2022-04-22 | 腾讯科技（深圳）有限公司 | Road data processing method, device, computer equipment and storage medium |
| US20230104188A1 (en) * | 2021-09-28 | 2023-04-06 | Here Global B.V. | Method, apparatus, and system for calibrating vehicle motion data based on mobile device sensor data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110054787A1 (en) * | 2009-08-27 | 2011-03-03 | Apple Inc. | Context Determination to Assist Location Determination Accuracy |
| CN201927175U (en) * | 2011-01-05 | 2011-08-10 | 中国科学院深圳先进技术研究院 | Information collector of intelligent transportation system |
| CN102409599A (en) * | 2011-09-22 | 2012-04-11 | 中国科学院深圳先进技术研究院 | Road surface detection method and system |
| CN102778239A (en) * | 2011-04-29 | 2012-11-14 | 江国庆 | Device and method for guiding inertia of fabric with calibration module |
| US20140005930A1 (en) * | 2012-06-29 | 2014-01-02 | Microsoft Corporation | Locating mobile devices |
-
2014
- 2014-09-24 CN CN201410495525.7A patent/CN104236566B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110054787A1 (en) * | 2009-08-27 | 2011-03-03 | Apple Inc. | Context Determination to Assist Location Determination Accuracy |
| CN201927175U (en) * | 2011-01-05 | 2011-08-10 | 中国科学院深圳先进技术研究院 | Information collector of intelligent transportation system |
| CN102778239A (en) * | 2011-04-29 | 2012-11-14 | 江国庆 | Device and method for guiding inertia of fabric with calibration module |
| CN102409599A (en) * | 2011-09-22 | 2012-04-11 | 中国科学院深圳先进技术研究院 | Road surface detection method and system |
| US20140005930A1 (en) * | 2012-06-29 | 2014-01-02 | Microsoft Corporation | Locating mobile devices |
Non-Patent Citations (2)
| Title |
|---|
| YAN WANG ET AL.: "Sensing Vehicle Dynamics for Determining Driver Phone Use", 《MOBISYS"13 PROCEEDING OF THE 11TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS,APPLICATIONS,AND SERVICES》 * |
| 林钰龙等: "基于Android智能手机的地图匹配算法研究", 《电子设计工程》 * |
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