CN102278995B - Bayes path planning device and method based on GPS (Global Positioning System) detection - Google Patents
Bayes path planning device and method based on GPS (Global Positioning System) detection Download PDFInfo
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Abstract
The invention relates to a Bayes path planning device and method based on GPS (Global Positioning System) detection. The method provided by the invention is a priori path finding method for simulating a driver by adopting a method for evaluating priori information by utilizing priori knowledge. The device provided by the invention contains a priori deduction method of the driver into the path planning device by utilizing a Bayes classifier, and the device is the path planning method formed after the patter recognition processing is carried out on a road network based on the classifier. In the invention, the Bayes classifier is constructed to form the prior recognition of the road network by utilizing the priori deduction of the driver on real-time traffic conditions of the road network; the path planning device and method formed based on prior recognition can respectively give different optimal paths according to the priori knowledge in rush hours and in non-rush hours at different traffic time intervals. The device and method provided by the invention have good practicality and can be flexibly integrated into various navigation systems based on road traffic.
Description
Technical field:
The present invention relates to a kind of a kind of path planning apparatus and method of adopting based on the deduction of priori of being suitable under the traffic trip situation of reality, particularly a kind of Bayes's path planning apparatus and method of surveying based on GPS.
Background technology:
Current, GPS (Global Positioning System) should use as positioning navigating device on various vehicles widely, such as onboard navigation system, and the locating and monitoring system of vehicle, floating vehicle system of traffic department etc.On the other hand, the optimal path that obtains of the method for optimizing route in our the various navigational system its path of planning out a kind of shortest path on absolute time or length measuring often in actual use.Yet because the random character of the real-time traffic of road, the optimal path on the tolerance meaning often is not the priori that meets so the driver like this, and such optimal path often is not the optimal path of our real world yet.
Patent (Chinese patent application number 200410103954.1) is that the time of vehicle and speed are merged a kind of method into path planning, patent (Chinese patent application number 200610036181.9) be a kind of be a kind of paths planning method that transit time is realized with the highway section weight setting, from top these several applications for patent, at present some have been arranged about the patent of path planning apparatus and method, existing patent about path planning only is a kind of path optimization method that provides in tolerance (length or time).These methods all do not have to adopt based on Bayesian priori estimating method comes realizing route planning.
Announced one piece of document " modeling of taxi experimental knowledge and path planning algorithm " in the volumes in " mapping journal " Augusts 39 in 2010 (404-409 page or leaf), although some has considered the posterior infromation of taxi, but only be some information to have been arrived under the road on the weights W with following computing formula reduction
I(t)=[μ
1T
I(t)+ μ
2S
i]/μ
3C
I (t)
In essence, this formula is that time and velocity information are passed through this formula, and the w weight setting to the highway section of correspondence, is calculated optimal path by traditional dijkstra method at last.The posterior infromation reduction is done the method for path planning to the road weights, in the situation that possess Real-time Traffic Information, be the same with the problem of traditional optimal path method, and it can't avoid the randomness of Real-time Traffic Information.The method is not a kind of priori estimating method, thereby also little in the method difference with the calculating of adopting the optimal path planning on the tolerance.Therefore, the document does not form a kind of driver's prior imformation and according to the Bayesian inference road collection of classifying, and carries out accordingly path planning.
Technical method from the existing path planning of upper surface analysis, these are with speed, length, the optimal path method of certain posterior infromation naturalization such as transit time on the tolerance of road weights, a kind of priori estimating method of the driver that really will not be familiar with very much the road network traffic is brought in the path planning system.
Summary of the invention:
Purpose of the present invention is exactly in view of the foregoing defects the prior art has, a kind of Bayes's path planning apparatus and method of surveying based on GPS is provided, and the inventive method is that a kind of priori that the method that adopts priori to estimate posterior information is simulated the driver is looked for path method; This device utilizes Bayes classifier that driver's priori estimating method is brought in our path planning apparatus, itself is based on the sorter device and road network is done the paths planning method that forms after the pattern recognition process.
Bayes's path planning apparatus of surveying based on GPS of the present invention mainly is divided into two parts of end of probe and center processing end, and wherein end of probe is selected the driver that some are familiar with road traffic condition, the GPS detector is distributed on these drivers' the vehicle; Described center processing end comprises receiver, central control unit, path screening washer, Bayes classifier and the several modules of optimal path computation device:
End of probe wherein: the GPS detector mainly is to come n driver's driving trace is surveyed with the GPS collector;
Described center processing end: central control unit, the data of controlling between other the module transmit; The path screening washer, the GPS tracing point of main these driver's vehicles according to collecting and starting point and the terminating point of collection recover the path of travelling, and filter out " good " path that Negotiation speed is fast, expend time in short; Bayes classifier is practiced Bayes classifier according to " good " path training of setting up, and according to the Bayes classifier that trains road is done classification, forms preferential road collection, suboptimum road collection and whole road collection; The optimal path computation device according to three kinds of preferred roads that form, is implemented optimum route search and is calculated, and obtains the optimal path result; Path memory is deposited " good " path collection that the path screening washer is set up; The classification of road storer is deposited three kinds of road collection that form after the Bayes classifier classification.
Bayes's paths planning method of surveying based on GPS of the present invention may further comprise the steps:
(1), end of probe is obtained the trip empirical data with the GPS detector;
(2), the center control end obtains track data and the realizing route planning by process a series ofly from the GPS detector, may further comprise the steps:
(2.1) obtain the path collection of " good " on the priori with the path screening washer: the GPS tracing point of main these driver's vehicles according to collecting and starting point and the terminating point of collection recover the path of travelling, and filter out " good " path that Negotiation speed is fast, expend time in short;
(2.2) practice Bayes classifier according to " good " path training of setting up, according to the Bayes classifier that trains road is done classification, form preferential road collection, suboptimum road collection and whole road collection;
(2.3) calculate optimal path in the optimal path computation device, central control unit is regarded the preferential road collection of above-mentioned three classes as the triple-decker network and is input to the optimal path computation device, obtains the optimal path result;
Above-mentioned path screening washer mainly is comprised of comparison controller, coupling computing module and buffer three parts:
A) in the coupling computing module, the driver's who collects according to end of probe GPS track and starting point and the terminating point of record, with map-matching method it is matched corresponding road section, and join according to adjacent road section and to obtain the path that forms between each starting point and the terminating point, the path collection that the coupling computing module obtains is input in the comparison controller;
B) in comparison controller, elder generation can obtain list according to the journey time of total length and the GPS record of every paths
The average passage rate v that paths superior rolling stock travels
Single, by with the mean value V of all path passage rates
Average overallCompare, according to v
Single〉=V
Average overallCan obtain the path of " good " on the priori, it is included in " good " the path collection, and put in the path memory.
Above-mentioned Bayes classifier mainly is made of data divider, roadway characteristic maker, Bayess classification controller, road topology detecting device and buffer;
A) " good " path is inputed to data divider, data divider is divided into " good " path collection T1 of peak period and " good " path collection T2 of non-peak period according to the time attribute in path with it;
B) in the roadway characteristic maker, to all " good " path collection, the frequency of all paths through same road section e taken statistics;
C) in the roadway characteristic maker, calculate on the road network all nodes between shortest path, and obtain thus the Betweenness Centrality of road section e.
D) from road network data, select a part out as the training data of Bayess classification controller, utilize the Bayess classification controller to come the road section classification is obtained preferential road collection, suboptimum road collection and Ordinary Rd collection;
E) in the road topology detecting device, according to three road collection that step d) obtains, the preferential road collection of this three class of its generation is put in the classification of road storer.
The invention has the beneficial effects as follows: this patent is exactly to utilize this driver that the priori of the real-time traffic situation of road network is inferred, construct Bayes classifier and formed preferential identification to road network, a kind of path planning apparatus and the method that form based on this preferential identification, it can provide respectively different optimal paths according to the priori of peak period, non-peak period in different traffic slots.This device and method has good practicality, and can be integrated into flexibly in the various navigational system based on road traffic.
Description of drawings:
Accompanying drawing 1 is the principle schematic of device of the present invention;
Accompanying drawing 2 is schematic diagram of path of the present invention screening washer;
Accompanying drawing 3 is the process flow diagrams in screening washer screening " good " path, path of the present invention;
Accompanying drawing 4 is schematic diagram of Bayes classifier of the present invention;
Accompanying drawing 5 is Bayess classification schematic flow sheets of road collection of the present invention;
Accompanying drawing 6 is optimal path computation process flow diagrams of the present invention.
Embodiment:
The concrete embodiment of this device is as follows:
The enforcement of end of probe:
Obtain the trip empirical data with the GPS detector.Select n the very familiar driver of area road net who needs is done path analysis, the GPS detector is contained on this n driver's the car, record the trip data of its every day with GPS, bag
Draw together starting point and the terminating point in its trip path.Cumulative record is after their trip path a period of time (such as one month) like this, according to GPS detector and the record thereof on these driver's vehicles that Real-time Road is familiar with, obtains n experience driver's trip GPS track data.
Center processing end implementation step:
The data transmission of whole center processing end intermodule is controlled by central control unit, and the steps flow chart of operation is also controlled by central control unit.The below provides Bayes's paths planning method that the center processing end is realized sequentially.
1 usefulness path screening washer obtains the path collection of " good " on the priori, and this module mainly is comprised of comparison controller, coupling computing module and buffer three parts, and its structural representation is seen Fig. 2, and its process flow diagram is seen Fig. 3;
The GPS track data that central control unit obtains gathering from the GPS detector.
1a) in the coupling computing module, the driver's who collects according to end of probe GPS track and starting point and the terminating point of record, with map-matching method it is matched corresponding road section, and join according to adjacent road section and to obtain the path that forms between each starting point and the terminating point.The path collection that the coupling computing module obtains is input in the comparison controller.
1b) in comparison controller, first according to the Hang journey Shi Jian ⊿ t between the total length L of every paths and this path are from the starting point to the terminating point, by v
Single=L/ ⊿ t can obtain the average passage rate v of individual paths
SingleThen calculate all travel speeds of GPS in a certain period T, according to the average V of the passage rate in all paths in this period
Average overallSet a threshold value.Compare v
SingleWith V
Average overallRelation.If v
Single〉=V
Average overall, think that then this path is the path of " good " on the priori, includes it in " good " the path collection, and put in the path memory; If v
Single﹤ V
Average overall, think that then this path is not the path of " good " on the priori, gives up it.After forming the path collection of be over " good " at last, the notice central control unit deposits the path collection of these " good " in the path memory in.
2 usefulness Bayes classifiers are done classification to road network, obtain three kinds of preferred road collection.Bayes classifier mainly is made of these several parts of data divider, roadway characteristic maker, Bayess classification controller, road topology detecting device and buffer, its structure as shown in Figure 4, its flow process is as shown in Figure 5.
" good " path collection that central control unit obtains from path memory is input to Bayes classifier.
2a) " good " path is inputed to data divider, data divider is divided into " good " path collection T1 of peak period and " good " path collection T2 of non-peak period according to the time attribute in path with it.Following step 2b--2e implements T1 and T2 respectively, obtains the preferential road collection of peak period three classes different with non-peak period.
2b) in the roadway characteristic maker, to all " good " path collection, the frequency of all paths through same road section e taken statistics, namely different paths is every through once identical road section, it is 0 that its frequency attribute f (e) adds 1, f (e) initial value.Do on average through the speed of road section according to all " good " paths, be designated as v (e).
2c) in the roadway characteristic maker, calculate on the road network all nodes between shortest path, all shortest paths add up to N.According to the number of times c (e) of all shortest paths through each road section e, the Betweenness Centrality B (e) of calculating highway section e=c (e)/N.
2d) from road network data, select a part out as the training data of Bayess classification controller.According to 2b) and 2c) in feature f (e), the v (e) of the road e that obtains and the parameter that B (e) estimates the Bayess classification controller.Determined after the Bayess classification controller parameter, utilized the Bayess classification controller to come the road section classification is obtained preferential road collection E according to its feature f (e), v (e) and B (e) in all roads
1, suboptimum road collection E
2With Ordinary Rd collection E
3
2e) in the road topology detecting device, according to step 2d) three road collection obtaining, from E
1Beginning calculates E
1The maximum strong subnet C that is communicated with
1With E
1In not at C
1Road again join E
2In, calculate E
2The maximum strong subnet C that is communicated with
2Like this, we make G
1=C
1As preferential road collection, make G
2=C
1+ C
2As suboptimum road collection, G
3Be all road collection of whole road networks conducts.Like this, we just obtain by G
1, G
2, G
3The preferential road collection of three classes that forms.
Central control unit is put into the preferential road collection of this three class of its generation in the classification of road storer after the step of Bayes classifier is finished.
3 according to the preferential road collection of the classification for peak period T1 and non-peak period T2 that forms in the step 2, calculates optimal path respectively in the optimal path computation device.Because for the road collection of T1 and T2, its calculation procedure all is the same, like this, in the following description T1 and T2 is not distinguished.Its flow process is seen Fig. 6.
Central control unit is regarded the preferential road collection of three classes as the triple-decker network and is input to the optimal path computation device.Determine optimal path to be determined starting point and ending point in road network.Several steps below in the optimal path computation device, implementing:
3a) from G
iBegin forward and reverse search optimal path from the initial sum terminating point respectively in the layer;
If 3b) at G
iIn layer forward and the reverse search, the node on the optimum subpath also is present in corresponding G
I-1In the layer, be designated as respectively S
I-1, T
I-1, so just jump back to step 3a), and i subtracts 1, namely at G
I-1Middle execution in step 3a).If in forward and reverse search, there is not such node on the optimum subpath, so with regard to execution in step 3c);
If 3c) subpath of the sub cost sum reverse search of forward lookup can not meet at same point, continue so execution in step 3b), if may converge a bit, execution in step 3d so);
The subpath that 3d) converges every layer of calculating forms last experience optimal path.
In this device, end of probe equipment GPS detector is assemblied on selected driver's vehicle with the form of hardware, and can preserve the GPS track data of its vehicle.The center processing end device can be under any operating system platform, utilize any programming language, utilize software mode to realize, also can adopt suitable hardware to realize, have good practicality, and can be integrated into flexibly in the various navigational system based on road traffic.
Claims (4)
1. Bayes's path planning apparatus of surveying based on GPS, it is characterized in that: mainly be divided into two parts of end of probe and center processing end, wherein end of probe is selected the driver that some are familiar with road traffic condition, the GPS detector is distributed on these drivers' the vehicle; Described center processing end comprises receiver, central control unit, path screening washer, Bayes classifier and the several modules of optimal path computation device:
End of probe wherein:
The GPS detector mainly is to come n driver's driving trace is surveyed with the GPS collector;
Described center processing end:
Central control unit, the data of controlling between other the module transmit;
The path screening washer, the GPS tracing point of main these driver's vehicles according to collecting and starting point and the terminating point of collection recover the path of travelling, and filter out " good " path that Negotiation speed is fast, expend time in short;
Bayes classifier is practiced Bayes classifier according to " good " path training of setting up, and according to the Bayes classifier that trains road is done classification, forms preferential road collection, suboptimum road collection and whole road collection;
The optimal path computation device according to three kinds of preferred roads that form, is implemented optimum route search and is calculated, and obtains the optimal path result;
Path memory is deposited " good " path collection that the path screening washer is set up;
The classification of road storer is deposited three kinds of road collection that form after the Bayes classifier classification.
2. Bayes's paths planning method of surveying based on GPS is characterized in that may further comprise the steps:
(1), end of probe is obtained the trip empirical data with the GPS detector;
(2), the control end track data that obtains from the GPS detector in center is through a series of processing and realizing route planning may further comprise the steps:
(2.1) obtain the path collection of " good " on the priori with the path screening washer: the GPS tracing point of main these driver's vehicles according to collecting and starting point and the terminating point of collection recover the path of travelling, and filter out " good " path that Negotiation speed is fast, expend time in short;
(2.2) practice Bayes classifier according to " good " path training of setting up, according to the Bayes classifier that trains road is done classification, form preferential road collection, suboptimum road collection and whole road collection;
(2.3) calculate optimal path in the optimal path computation device, central control unit is regarded the preferential road collection of above-mentioned three classes as the triple-decker network and is input to the optimal path computation device, obtains the optimal path result.
3. Bayes's paths planning method of surveying based on GPS according to claim 2, it is characterized in that: described path screening washer mainly is comprised of comparison controller, coupling computing module and buffer three parts:
A) in the coupling computing module, the driver's who collects according to end of probe GPS track and starting point and the terminating point of record, with map-matching method it is matched corresponding road section, and join according to adjacent road section and to obtain the path that forms between each starting point and the terminating point, the path collection that the coupling computing module obtains is input in the comparison controller;
B) in comparison controller, elder generation is according to the journey time of total length and the GPS record of every paths, can obtain its average passage rate v, relation by average passage rate, obtain the path of " good " on the priori, it is included in " good " the path collection, and put in the path memory.
4. Bayes's paths planning method of surveying based on GPS according to claim 2, it is characterized in that: described Bayes classifier mainly is made of data divider, roadway characteristic maker, Bayess classification controller, road topology detecting device and buffer;
A) " good " path is inputed to data divider, data divider is divided into " good " path collection T1 of peak period and " good " path collection T2 of non-peak period according to the time attribute in path with it;
B) in the roadway characteristic maker, to all " good " path collection, the frequency of all paths through same road section e taken statistics;
C) in the roadway characteristic maker, calculate on the road network all nodes between shortest path, and obtain thus the Betweenness Centrality of road section e;
D) from road network data, select a part out as the training data of Bayess classification controller, utilize the Bayess classification controller to come the road section classification is obtained preferential road collection, suboptimum road collection and Ordinary Rd collection;
E) in the road topology detecting device, according to three road collection that step d) obtains, the preferential road collection of this three class of its generation is put in the classification of road storer.
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| US10274336B2 (en) | 2012-06-06 | 2019-04-30 | Apple Inc. | Context aware map application |
| CN102809384B (en) * | 2012-08-22 | 2015-11-11 | 北京世纪高通科技有限公司 | A kind of road chain extracting method and device |
| CN103714708A (en) * | 2013-12-18 | 2014-04-09 | 福建工程学院 | Optimal path planning method based on split-time experience path of taxi |
| CN104062671B (en) * | 2014-07-08 | 2016-07-13 | 中国石油大学(华东) | The GNSS Floating Car map-matching method of curvature limitation and device |
| CN104990559B (en) * | 2015-07-27 | 2018-08-21 | 福建工程学院 | A kind of path recommendation method, system and client based on taxi empirical data |
| CN107346478A (en) * | 2016-05-04 | 2017-11-14 | 中国农业大学 | Shipping paths planning method, server and system based on historical data |
| EP3339807B1 (en) * | 2016-12-20 | 2024-03-13 | HERE Global B.V. | An apparatus and associated methods for determining the location of a vehicle |
| WO2018165848A1 (en) * | 2017-03-14 | 2018-09-20 | 深圳市南北汽车美容有限公司 | Method for analysing real-time road conditions to recommend travel time, and navigation system |
| WO2018227374A1 (en) | 2017-06-13 | 2018-12-20 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for route planning based on deep convolutional neural network |
| CN107766881B (en) * | 2017-09-30 | 2020-06-26 | 中国地质大学（武汉） | Way finding method and device based on basic classifier and storage device |
| CN107941234A (en) * | 2017-12-21 | 2018-04-20 | 广东欧珀移动通信有限公司 | Recommendation method, apparatus, storage medium and the mobile terminal of traffic path |
| CN110033642A (en) * | 2019-04-18 | 2019-07-19 | 福建工程学院 | A kind of indoor shared parking stall path experience generates and its bootstrap technique |
| CN111754800B (en) * | 2020-04-13 | 2021-10-15 | 腾讯科技（深圳）有限公司 | Method and device for acquiring road condition information and server |
| CN111858817B (en) * | 2020-07-23 | 2021-05-18 | 中国石油大学(华东) | BilSTM-CRF path inference method for sparse track |
| CN111829527B (en) * | 2020-07-23 | 2021-07-20 | 中国石油大学(华东) | Unmanned ship path planning method based on deep reinforcement learning and considering marine environment elements |
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| US7813870B2 (en) * | 2006-03-03 | 2010-10-12 | Inrix, Inc. | Dynamic time series prediction of future traffic conditions |
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