CN103680185A - Vehicle traveling road level precise division method - Google Patents
Vehicle traveling road level precise division method Download PDFInfo
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Abstract
The invention relates to the technical field of vehicle traveling road level division. According to the vehicle traveling road level precise division method disclosed by the invention, a precise vehicle traveling road level method is provided. The vehicle traveling road level precise division method comprises the steps of: obtaining traveling data of a vehicle in real time, calculating matched road information based on the traveling data and navigation map data, calculating most possible traveling road information of the vehicle in a first time section deltaT1 by using a statistical calculation method, and further obtaining a correct traveling road level of the vehicle in complex and interferential roads through a vehicle traveling road level judgment method.
Description
Technical field
The present invention relates to Vehicle Driving Cycle category of roads partitioning technology field, be specifically related to the accurate division methods of a kind of Vehicle Driving Cycle category of roads, can be for come control engine industry control to carry out fuel-saving control according to category of roads.
Background technology
Along with automobile is universal, intercity road develops rapidly, automobile has become the important tool of life, and automobile vendor is also faced with the very large market space of tool.Same, when becoming everybody, automobile lives after indispensable instrument, and everyone driving habits is also often different, and the today more and more highlighting in energy problem, environmental problem, how fuel-economizing becomes an important problem.For automobile, engine is heart, is determining the power of automobile, in low-grade situation, if engine speed is too high, easily cause idle running, energy dissipation is settled and can not be brought corresponding power, otherwise, when in top gear situation, if rotating speed is too low, deeply step on the gas, but can not bring corresponding power.From general knowledge, when running car is during in expressway, in the depth action that the rotating speed of engine should stepped on the gas at immediate response, while deeply stepping on the gas, need to obtain immediately power, by the conversion of energy maximum on power.When running car is on urban road, because vehicle is more, gear is in more low-grade substantially, deeply steps on the gas and must bring the waste of energy.Therefore, for the good car owner of driving habits, the impact that above problem may be brought not is very large, but for the poor car owner of driving habits, above problem just may be brought very large oil consumption waste.Therefore, automobile vendor carrys out the range of speeds of control engine with regard to the road that has proposed how to travel by definite car owner, even if allow the car owner of some bad customs still repeat bad habit, also can maximumly improve fuel-saving efficiency.But the problem of most critical is exactly road, be that wrong ancestor is complicated, when you travel on a road, inevitable periphery exists very many road informations, how to mate vehicle to correct road, and send to engine industry control just to seem most important in order to the control engine range of speeds, if the category of roads information of obtaining is wrong or variation repeatedly, will bring unnecessary engine scope adjustment, not only can not reach fuel-economizing object, also can affect and drive and bring certain danger.
Summary of the invention
Solve the problems of the technologies described above, the invention provides the accurate division methods of a kind of Vehicle Driving Cycle category of roads, by obtaining Vehicle Driving Cycle category of roads, accurately divide, in the road disturbing in complexity, obtain correct travel grade, the present invention is applicable to come control engine industry control to carry out fuel-saving control according to category of roads, by in time category of roads data being sent to engine industry control module, by knowing the category of roads under current Vehicle Driving Cycle, adjust in time engine industry control, in order to control vehicle, on road, be in the optimal engine range of speeds, allow automobile can automatically regulate engine speed range on different brackets road, correct some bad driving habitses, thereby reach the object of fuel-economizing.
In order to achieve the above object, the technical solution adopted in the present invention is that the accurate division methods of a kind of Vehicle Driving Cycle category of roads, comprises the following steps:
Step 1: obtain the running data of vehicle, this running data comprises current location, speed, directional information;
Step 2: the running data obtaining according to the road topology data that provide in navigation map data and step 1, calculate the mRoadInfo[n of the road information of coupling] array, this mRoadInfo[n] data type of array is RoadInfo, next distributor road array that the unique GUID sign of class information Level, road that wherein RoadInfo data type comprises road and data type are RoadInfo
Step 3: traversal mRoadInfo[n] each array object in array, according to the unique GUID sign of the road of each array object traversal road topology data, obtain every road information along next crossing distributor road data of vehicle heading, obtain mRoadNextInfo[] array, this mRoadNextInfo[] array type is RoadInfo, for travel is next time determined and is predicted.
Step 4: adopt statistical calculation method to calculate vehicle in the most probable travel information of very first time section △ T1, and give by the most probable travel information of this very first time section △ T1 assignment the road information mPreRoadInfo that initially travels;
Step 5: adopt statistical calculation method to calculate vehicle in the most probable travel information of the second time period △ T2, and by the most probable travel information of this second time period △ T2 assignment to current driving road information mCurRoadInfo;
Step 6: after having determined current road mCurRoadInfo, starting to carry out Vehicle Driving Cycle category of roads determines, whether the road unique identification GUID that judges each object in the set of mCurRoadInfo is consistent with the road unique identification GUID of the category of roads of first object of the road information mPreRoadInfo that initially travels or next crossing distributor road of first object, if, by mCurRoadInfo assignment to mPreRoadInfo, successfully obtain current category of roads, return to step 5; Otherwise output error, and mError parameter is added up to 1, execution step 7;
Step 7: as mError >=X, remove mPreRoadInfo, mError, return to step 4 and carry out, otherwise re-execute step 5.
Further, calculate the mRoadInfo[of the road information of coupling] step of array is as follows:
1) traversal road topology data, obtain the current location road data set in M kilometer rectangular area around, and M value is 1<=M<=2(kilometer);
2) traversal road data set, the curtate distance of the straight line that rejecting vehicle current location is connected with road starting and terminal point, from the road that is greater than 30 meters, obtains the set of second circuit-switched data, the error of bringing with control position drift;
3) traversal second circuit-switched data set, the included angle of straight line that rejecting vehicle heading is connected with road starting and terminal point is greater than the road of 120 degree, obtains the 3rd road data set, assert that these roads are reverse road;
4) road data in the 3rd road data set is sorted by distance is far and near, obtains mRoadInfo[n] (n=0,1,2 ..., n is positive integer) and array.
Further, in described step 4 and step 5, statistical calculation method is to calculate vehicle at the algorithm of the most probable travel information of time period △ T, and this statistical calculation method is specific as follows:
1) definition road statistics array Sta[], this array Sta[] comprise N object, this Sta[] array type is RoadInfo;
2) mRoadInfo[calculating every cycle 1S by step 2 method] array element sequentially inputs Sta[] in array;
3) judgement Sta[N-1], Sta[N-2] ... Sta[N-M-1] in the mRoadInfo[0 of each element] grade whether consecutive identical, be to return to Sta[N-1] as most probable travel information, otherwise finish this statistics.
?further, in described step 6, the definite determination step of Vehicle Driving Cycle category of roads is specific as follows:
1) if exist the unique GUID sign of the road of object β identical with the unique GUID sign of the road sign of first object of mPreRoadInfo collection object in mCurRoadInfo collection object, think that vehicle still travels on same path, category of roads remains unchanged, and by mCurRoadInfo assignment to mPreRoadInfo, if the object β of mCurRoadInfo is not in first object of set, object β need to be adjusted into first object of set, return to step 5;
2) if there is the unique GUID sign of the road of object α and mPreRoadInfo distributor road collection object mRoadNextInfo[in mCurRoadInfo set in object] the unique GUID sign of road of any one object identify identical, think that Vehicle Driving Cycle is on new road, by this assignment of mCurRoadInfo to mPreRoadInfo, and upgrade the grade that category of roads is first object of mPreRoadInfo, return to step 5;
3) if do not meet above-mentioned 1) 2) condition, output error, and mError parameter is added up to 1, execution step 7.
Further, wherein RoadInfo data type comprises int level, long guid and RoadInfo mRoadNextInfo[], described int level represents the affiliated grade of this road, described long guid represents the unique GUID of this road sign, described RoadInfo mRoadNextInfo[] represent next crossing distributor road data of this road.RoadInfo dtd-data type definition is as follows:
| int level | Grade under this road |
| long guid | The unique GUID sign of this road |
| RoadInfo mRoadNextInfo[] | Next crossing distributor road data of this road |
Further, in described step 2, the class information Level of road comprises expressway, city expressway, national highway, provincial highway and Ordinary Rd.
Further, described navigation map data comprises high moral map, clever figure map and hundred million figure earthing drawing systems.
Further, described very first time section △ T1 and the second time period △ T2 sequentially sample according to time shaft.
The present invention is by adopting technique scheme, and compared with prior art, tool has the following advantages:
The invention provides Vehicle Driving Cycle category of roads method accurately, by the running data of Real-time Obtaining vehicle, according to running data and navigation map data, calculate the road information mating, and adopt statistical calculation method to calculate vehicle in the most probable travel information of time period △ T, and then by Vehicle Driving Cycle category of roads determination methods, in the road disturbing in complexity, obtain the correct travel grade of vehicle.
Accompanying drawing explanation
Fig. 1 is the road topology structural representation of embodiments of the invention;
Fig. 2 is engine speed and the corresponding table of category of roads of the embodiment of the present invention.
Embodiment
Now the present invention is further described with embodiment by reference to the accompanying drawings.
As a specific embodiment, as shown in Figure 1, the accurate division methods of a kind of Vehicle Driving Cycle category of roads of the present invention, comprises the following steps:
Step 1: obtain the running data of vehicle, the running data of vehicle obtains by vehicle GPS module.This step specific implementation process, with reference to the road topology structure shown in figure 1, one vehicle W is from a road s point, to road c direction running, road d is for producing the interference road of positioning error, and the Nodes of road a and road c has a branch outlet b, vehicle launch, vehicle starts to travel on the way from s point, and interval 1S obtains the running data of this vehicle, and this running data comprises current location, speed, directional information.
Step 2: the running data obtaining according to the road topology information providing in navigation map data and step 1, calculate the mRoadInfo[of the road information of coupling] array, this mRoadInfo[] data type of array is RoadInfo, the unique GUID sign of class information Level, road that wherein RoadInfo object comprises road, next distributor road array that type is RoadInfo etc., the class information Level of road comprises expressway, city expressway, national highway, provincial highway and Ordinary Rd.Described navigation map data comprises high moral map, clever figure map and hundred million figure earthing drawing systems.
Wherein RoadInfo dtd-data type definition is as follows:
| int level | Grade under this road |
| long guid | The unique GUID sign of this road |
| RoadInfo mRoadNextInfo[] | Next crossing distributor road data of this road |
Suppose that vehicle current location is at a road waypoint p, the road information step of calculating coupling is as follows:
1) traversal road topology data, obtain the current location p road data S set ET in M kilometer rectangular area around, and M value is got 1(kilometer), the road data set-inclusion object obtaining is SET={ a, b, c, d, e, f}.The desirable 1<=M<=2(kilometer of M value scope);
2) travel through above-mentioned road data S set ET={ a, b, c, d, e, f}, rejects the curtate distance of the straight line that vehicle current location is connected with road starting and terminal point from the road that is greater than 30 meters, the error of drifting about and bringing with control position, the road data set-inclusion object obtaining is SET={ a, d, e};
3) travel through above-mentioned road data S set ET={ a, d, e}, the included angle of straight line that rejecting vehicle heading is connected with road starting and terminal point is greater than the road of 120 degree, assert that these roads are reverse road, the road data set-inclusion object obtaining is SET={ a, d}.
4) by road data S set ET=a, the road in d}, by the far and near sequence of distance, finally obtains mRoadInfo[n] (n=0,1,2 ..., n is positive integer) and array, i.e. mRoadInfo={a, d}.
Step 3: traversal mRoadInfo[n] each array object in array, according to the unique GUID sign of the road of each array object traversal road topology data, obtain every road information along next crossing distributor road data of vehicle heading, obtain mRoadNextInfo[] array, this mRoadNextInfo[] array type is RoadInfo, for travel is next time determined and is predicted.
Shown in figure 1, mRoadInfo={a, the mRoadNextInfo[of each array object in d} array] as follows:
1) mRoadNextInfo[of branch of d]={ null}
2) mRoadNextInfo[of branch of a]={ c, b}
Step 4: according to mRoadInfo[] array and its corresponding mRoadNextInfo[] array, adopt statistical calculation method to calculate vehicle in the most probable travel information of very first time section △ T1, and give by the most probable travel information of this very first time section △ T1 assignment the road information mPreRoadInfo that initially travels, and determine that initial category of roads is the grade point of first object of mPreRoadInfo:
In order better to illustrate, describe the statistical calculation method using in step 4 and step 5 in detail by the present embodiment, statistical calculation method is to calculate vehicle at the algorithm of the most probable travel information of time period △ T, and this statistical calculation method is specific as follows:
1) definition road statistics array Sta[], this array Sta[] comprise N object, this Sta[] array type is RoadInfo;
2) mRoadInfo[obtaining every cycle 1S by step 2 method] array element sequentially inputs Sta[] in array;
3) judgement Sta[N-1], Sta[N-2] ... Sta[N-M-1] in the road information object mRoadInfo[0 of each element] category of roads whether consecutive identical, be to return to Sta[N-1] as most probable travel information, otherwise finish this statistics.
Shown in figure 1, suppose that vehicle still travels on a and away from b, on c node, as s point, adopt step 2 method, the mRoadInfo[that a gap periods 1S continuous acquisition N step 2 obtains] collection object input Sta[], work as Sta[] mRoadInfo[0 of object in array] Equicontinuous occur that M the identical and unique GUID of road is designated a road, assert that Vehicle Driving Cycle is on a road, and by the mRoadInfo[gathering for the last time] aggregate assignment gives initial road mPreRoadInfo road object, mPreRoadInfo={ a here, d}, the mRoadNextInfo[of mPreRoadInfo object]={ c, b},
Step 5: adopt statistical calculation method to calculate vehicle in the most probable travel information of the second time period △ T2, and by the most probable travel information of this second time period △ T2 assignment to current driving road information mCurRoadInfo; Described very first time section △ T1 and the second time period △ T2 sequentially sample according to time shaft;
1) suppose that Vehicle Driving Cycle has arrived p point, adopt statistical calculation method to calculate mCurRoadInfo={ a, d};
2) suppose that Vehicle Driving Cycle has arrived q point, adopt statistical calculation method to calculate mCurRoadInfo={ c, d}, but under interference environment, also have mCurRoadInfo={ d, the c} situation that calculate.
Step 6: after having determined current road mCurRoadInfo, starting to carry out Vehicle Driving Cycle category of roads determines, its principle is to judge that whether the road unique identification GUID of each object in the set of mCurRoadInfo is consistent, specific as follows with the road unique identification GUID of the category of roads of first object of the road information mPreRoadInfo that initially travels or next crossing distributor road of first object:
1) if exist the unique GUID sign of the road of object β identical with the unique GUID sign of the road sign of first object of mPreRoadInfo collection object in mCurRoadInfo collection object, think that vehicle still travels on same path, category of roads remains unchanged, by mCurRoadInfo assignment to mPreRoadInfo, if the object β of mCurRoadInfo is not in first object of set, object β need to be adjusted into first object of set.For example, in step 5 1), mCurRoadInfo collection object a is identical with first object of mPreRoadInfo collection object a, and the unique GUID sign of road is identical, thinks that vehicle still travels on a road;
2) if there is the unique GUID sign of the road of object α and mPreRoadInfo distributor road collection object mRoadNextInfo[in mCurRoadInfo set in object] the unique GUID sign of road of any one object identify identical, think that Vehicle Driving Cycle is on new road, by this assignment of mCurRoadInfo to mPreRoadInfo, and upgrade the grade that category of roads is first object of mPreRoadInfo.For example, in step 5 2) in CurRoadInfo collection object c with mPreRoadInfo distributor road collection object mRoadNextInfo[] in c identical, and b does not belong to mRoadNextInfo[] think to disturb road, assert that vehicle starts to travel on c, the category of roads value that renewal category of roads is c; Return to step 5;
3) if do not meet above-mentioned 1) 2) condition, output error, and mError parameter is added up to 1, execution step 7.
[0025]step 7: as mError>=X, 3<=X<=5, removes PreRoadInfo, mError, returns to step 4 and carries out, otherwise re-execute step 5.
By said method, having obtained Vehicle Driving Cycle category of roads accurately divides, this category of roads is fed back to engine industry control, just can engine industry control will be adjusted in time, control vehicle and on road, be in the optimal engine range of speeds, allow automobile can automatically regulate engine speed range on different brackets road; With reference to the engine speed in figure 2 and the corresponding table of category of roads
| Category of roads | Rotating speed |
| 1 grade (at a high speed, fast) | 3000 ~ 5000 turn |
| 2 grades (national highway, provincial highway, city main trunk road) | 2000 ~ 3000 turn |
| 3 grades (other Ordinary Rd) | 800 ~ 2000 turn |
Suppose above-mentioned steps 6 1), 2) all cannot determine current driving road, by this road driving choose regard as abnormal, cumulative errors number Error.
When Error is less than limit value X (3<=X<=5), restart step 5, obtain Vehicle Driving Cycle road information next time.
When Error equals greatly limit value X, data operation module is regarded as extremely, thinks that vehicle has lost the scope of prediction, need to redefine the initial travel of vehicle, returns to step 4 and carries out, and starts a new road and selectes.
Due to category of roads difference, Vehicle Driving Cycle is on different brackets road, the speed of a motor vehicle, road conditions all there are differences, for the M value in step C is selected, also need to select different value as condition, suppose when Vehicle Driving Cycle is during in expressway, have a lot of service areas on the way, for fear of normally travelling expressway and wrong being mapped on service area road, need to strengthen M value, then in conjunction with the speed of a motor vehicle of expressway, can not cause described interference.Note, when vehicle is in expressway, M value is got X/2 conventionally, so effectively avoids this situation of service area, guarantees at highway statistical accuracy, and other grade road is got X/3.
We are bright is to using initial travel as the selected condition of next road information, therefore very strict, therefore selected for initial travel for the selected correctness requirement of initial travel, requires the M value in step 4 to get 2*X/3.
Although specifically show and introduced the present invention in conjunction with preferred embodiment; but those skilled in the art should be understood that; within not departing from the spirit and scope of the present invention that appended claims limits; can make a variety of changes the present invention in the form and details, be protection scope of the present invention.
Claims (8)
1. the accurate division methods of Vehicle Driving Cycle category of roads, is characterized in that: comprise the following steps:
Step 1: obtain the running data of vehicle, this running data comprises current location, speed, directional information;
Step 2: the running data obtaining according to the road topology data that provide in navigation map data and step 1, calculate the mRoadInfo[n of the road information of coupling] array, this mRoadInfo[n] data type of array is RoadInfo, next distributor road array that the unique GUID sign of class information Level, road that wherein RoadInfo data type comprises road and data type are RoadInfo
Step 3: traversal mRoadInfo[n] each array object in array, according to the unique GUID sign of the road of each array object traversal road topology data, obtain every road information along next crossing distributor road data of vehicle heading, obtain mRoadNextInfo[] array, this mRoadNextInfo[] array type is RoadInfo;
Step 4: adopt statistical calculation method to calculate vehicle in the most probable travel information of very first time section △ T1, and give by the most probable travel information of this very first time section △ T1 assignment the road information mPreRoadInfo that initially travels;
Step 5: adopt statistical calculation method to calculate vehicle in the most probable travel information of the second time period △ T2, and by the most probable travel information of this second time period △ T2 assignment to current driving road information mCurRoadInfo;
Step 6: whether the road unique identification GUID that judges each object in the set of mCurRoadInfo is consistent with the road unique identification GUID of the category of roads of first object of the road information mPreRoadInfo that initially travels or next crossing distributor road of first object, if, by mCurRoadInfo assignment to mPreRoadInfo, successfully obtain current category of roads, return to step 5; Otherwise output error, and mError parameter is added up to 1, execution step 7;
Step 7: as mError >=X, remove mPreRoadInfo, mError, return to step 4 and carry out, otherwise re-execute step 5.
2. the accurate division methods of a kind of Vehicle Driving Cycle category of roads according to claim 1, is characterized in that: in described step 2, calculate the mRoadInfo[of the road information of coupling] step of array is as follows:
(1) traversal road topology data, obtain the current location road data set in M kilometer rectangular area around, and M value is 1<=M<=2(kilometer);
(2) traversal road data set, the curtate distance of the straight line that rejecting vehicle current location is connected with road starting and terminal point, from the road that is greater than 30 meters, obtains the set of second circuit-switched data, the error of bringing with control position drift;
(3) traversal second circuit-switched data set, the included angle of straight line that rejecting vehicle heading is connected with road starting and terminal point is greater than the road of 120 degree, obtains the 3rd road data set, assert that these roads are reverse road;
(4) road data in the 3rd road data set is sorted by distance is far and near, obtains mRoadInfo[n] (n=0,1,2 ..., n is positive integer) and array.
3. the accurate division methods of a kind of Vehicle Driving Cycle category of roads according to claim 1, it is characterized in that: in described step 4 and step 5, statistical calculation method is to calculate vehicle at the algorithm of the most probable travel information of time period △ T, and this statistical calculation method is specific as follows:
(1) definition road statistics array Sta[], this array Sta[] comprise N object, this Sta[] array type is RoadInfo;
(2) mRoadInfo[calculating every cycle 1S by step 2 method] array element sequentially inputs Sta[] in array;
(3) judgement Sta[N-1], Sta[N-2] ... Sta[N-M-1] in the mRoadInfo[0 of each element] grade whether consecutive identical, be to return to Sta[N-1] as most probable travel information, otherwise finish this statistics.
4. the accurate division methods of a kind of Vehicle Driving Cycle category of roads according to claim 1, is characterized in that: in described step 6, the definite determination step of Vehicle Driving Cycle category of roads is as follows:
(1) if exist the unique GUID sign of the road of object β identical with the unique GUID sign of the road sign of first object of mPreRoadInfo collection object in mCurRoadInfo collection object, think that vehicle still travels on same path, category of roads remains unchanged, and by mCurRoadInfo assignment to mPreRoadInfo, if the object β of mCurRoadInfo is not in first object of set, object β need to be adjusted into first object of set, return to step 5;
(2) if there is the unique GUID sign of the road of object α and mPreRoadInfo distributor road collection object mRoadNextInfo[in mCurRoadInfo set in object] the unique GUID sign of road of any one object identify identical, think that Vehicle Driving Cycle is on new road, by this assignment of mCurRoadInfo to mPreRoadInfo, and upgrade the grade that category of roads is first object of mPreRoadInfo, return to step 5;
(3) if do not meet above-mentioned (1) (2) condition, output error, and mError parameter is added up to 1, execution step 7.
5. the accurate division methods of a kind of Vehicle Driving Cycle category of roads according to claim 1, it is characterized in that: RoadInfo data type comprises int level, long guid and RoadInfo mRoadNextInfo[], described int level represents the affiliated grade of this road, described long guid represents the unique GUID of this road sign, described RoadInfo mRoadNextInfo[] represent next crossing distributor road data of this road.
6. the accurate division methods of a kind of Vehicle Driving Cycle category of roads according to claim 1, is characterized in that: in described step 2, the class information Level of road comprises expressway, city expressway, national highway, provincial highway and Ordinary Rd.
7. the accurate division methods of a kind of Vehicle Driving Cycle category of roads according to claim 1, is characterized in that: described navigation map data comprises high moral map, clever figure map and hundred million figure earthing drawing systems.
8. the accurate division methods of a kind of Vehicle Driving Cycle category of roads according to claim 1, is characterized in that: described very first time section △ T1 and the second time period △ T2 sequentially sample according to time shaft.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108898833A (en) * | 2018-07-11 | 2018-11-27 | 贵州交通信息与应急指挥中心 | A kind of high speed congestion space-time analysis method |
| CN108924451A (en) * | 2017-05-09 | 2018-11-30 | 高德信息技术有限公司 | A kind of image acquiring method and device |
| CN109345854A (en) * | 2018-10-30 | 2019-02-15 | 江铃汽车股份有限公司 | Automobile speed limit reminding method, device, readable storage medium storing program for executing and automobile |
| CN109523781A (en) * | 2018-10-10 | 2019-03-26 | 中国电子科技集团公司第二十八研究所 | A kind of crossing prediction technique based on satellite positioning |
| CN111610787A (en) * | 2020-05-29 | 2020-09-01 | 亚哲科技股份有限公司 | Method for controlling safety of automatically driven automobile, computer storage medium and electronic device |
| CN112836144A (en) * | 2021-02-03 | 2021-05-25 | 北京中交兴路信息科技有限公司 | Method and system for searching vehicle with specified route based on track points |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008117787A1 (en) * | 2007-03-27 | 2008-10-02 | Nec Corporation | Map matching system, map matching method and program |
| KR20090050292A (en) * | 2007-11-15 | 2009-05-20 | (주)엠앤소프트 | High-level road map matching system and method |
| CN101488158A (en) * | 2009-02-13 | 2009-07-22 | 同济大学 | Road network modeling method based on road element |
| JP4418828B2 (en) * | 2007-06-29 | 2010-02-24 | 株式会社三菱総合研究所 | Map matching device, map matching method and program thereof |
| CN102147260A (en) * | 2010-02-09 | 2011-08-10 | 日电（中国）有限公司 | Electronic map matching method and device |
| TW201231927A (en) * | 2011-01-31 | 2012-08-01 | Tomtom Germany Gmbh & Co Kg | Graph based topological map matching |
| CN103149576A (en) * | 2013-01-29 | 2013-06-12 | 武汉大学 | Map matching method of floating car data |
| US8489316B1 (en) * | 2012-06-28 | 2013-07-16 | Delphi Technologies, Inc. | Map matching method for vehicle safety warning system |
-
2013
- 2013-12-25 CN CN201310725338.9A patent/CN103680185B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008117787A1 (en) * | 2007-03-27 | 2008-10-02 | Nec Corporation | Map matching system, map matching method and program |
| JP4418828B2 (en) * | 2007-06-29 | 2010-02-24 | 株式会社三菱総合研究所 | Map matching device, map matching method and program thereof |
| KR20090050292A (en) * | 2007-11-15 | 2009-05-20 | (주)엠앤소프트 | High-level road map matching system and method |
| CN101488158A (en) * | 2009-02-13 | 2009-07-22 | 同济大学 | Road network modeling method based on road element |
| CN102147260A (en) * | 2010-02-09 | 2011-08-10 | 日电（中国）有限公司 | Electronic map matching method and device |
| TW201231927A (en) * | 2011-01-31 | 2012-08-01 | Tomtom Germany Gmbh & Co Kg | Graph based topological map matching |
| US8489316B1 (en) * | 2012-06-28 | 2013-07-16 | Delphi Technologies, Inc. | Map matching method for vehicle safety warning system |
| CN103149576A (en) * | 2013-01-29 | 2013-06-12 | 武汉大学 | Map matching method of floating car data |
Non-Patent Citations (2)
| Title |
|---|
| 夏州: "GPS车辆导航中的数据处理与地图匹配研究", 《中国硕士学位论文全文数据库》 * |
| 范铁虎: "道路参数获取及其在AMT换档策略中应用的研究", 《中国博士学位论文全文数据库》 * |
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| CN108924451A (en) * | 2017-05-09 | 2018-11-30 | 高德信息技术有限公司 | A kind of image acquiring method and device |
| CN108898833A (en) * | 2018-07-11 | 2018-11-27 | 贵州交通信息与应急指挥中心 | A kind of high speed congestion space-time analysis method |
| CN109523781A (en) * | 2018-10-10 | 2019-03-26 | 中国电子科技集团公司第二十八研究所 | A kind of crossing prediction technique based on satellite positioning |
| CN109523781B (en) * | 2018-10-10 | 2020-07-28 | 中国电子科技集团公司第二十八研究所 | Intersection prediction method based on satellite positioning |
| CN109345854A (en) * | 2018-10-30 | 2019-02-15 | 江铃汽车股份有限公司 | Automobile speed limit reminding method, device, readable storage medium storing program for executing and automobile |
| CN109345854B (en) * | 2018-10-30 | 2021-10-08 | 江铃汽车股份有限公司 | Automobile speed limit prompting method and device, readable storage medium and automobile |
| CN111610787A (en) * | 2020-05-29 | 2020-09-01 | 亚哲科技股份有限公司 | Method for controlling safety of automatically driven automobile, computer storage medium and electronic device |
| CN111610787B (en) * | 2020-05-29 | 2023-04-07 | 亚哲科技股份有限公司 | Method for controlling safety of automatically driven automobile, computer storage medium and electronic device |
| CN112836144A (en) * | 2021-02-03 | 2021-05-25 | 北京中交兴路信息科技有限公司 | Method and system for searching vehicle with specified route based on track points |
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