CN101808275A - Vehicle network data transmitting method based on vehicle movement trend prediction - Google Patents

Abstract

The invention discloses a kind of In-vehicle networking data forwarding methods based on the prediction of vehicle mobile trend, belong to wireless communication field. The method of the present invention includes: that whithin a period of time, its track data when driving of vehicle registration obtains a data set; Later, when close to each other between vehicle, respective mobile trend is calculated, vehicle-mounted data is forwarded from high to low by mobile trend, is finally forwarded to base station when by base station by the lower vehicle of mobile trend; The mobile trend is calculated by formula (4): , wherein the mobile trend of Ti (M) expression vehicle; M indicates the street transfer number of setting; S indicates street set; Pij (m) indicates that vehicle is reached the probability of another street j by current street i after m step transfer, which collects calculating based on the data; Indicate another street and the Euclidean distance between nearest base station. The present invention can be used for the technical applications such as In-vehicle networking.

Description

A kind of In-vehicle networking data forwarding method based on the prediction of vehicle mobile trend

Technical field

The present invention relates to In-vehicle networking, relate in particular to a kind of In-vehicle networking data forwarding method, belong to wireless communication field based on the prediction of vehicle motion track.

Background technology

In-vehicle networking (VANET) utilizes the Wireless Telecom Equipment of generally installing on the vehicle, and as 802.11 wireless network cards, composition has the network configuration of high mobility, broad covered area, utilizes vehicle to move mutually and finishes data forwarding near the of short duration communication opportunity that forms.Each research institution has researched and developed a series of representational In-vehicle networking prototype systems in recent years, CarTel (Bret Hull for example, Vladimir Bychkovsky, Kevin Chen, Michel Goraczko, Allen Miu, Eugene Shih, Yang Zhang, Hari Balakrishnan, and Samuel Madden, " CarTel:A Distributed Mobile SensorComputing System. " in Proc.ACM SenSys, 2006) propose to utilize bus to collect the perception data of roadside fixation of sensor network; Pothole Patrol (Jakob Eriksson, Lewis Girod, Bret Hull, Ryan Newton, SamuelMadden, Hari Balakrishnan, " The Pothole Patrol:Using a Mobile Sensor Network for RoadSurface Monitoring. " in MobiSys, 2008) propose to gather pavement behavior information at vehicle upper administration acceleration transducer.These are used prototype system and show that the In-vehicle networking data forwarding method has important application prospects.

Whether rely on onboard navigation system (Navigation System) according to data forwarding and existing In-vehicle networking data forwarding method can be divided into two big classes: (1) is based on the data forwarding method in geographical position; (2) based on the data forwarding method of onboard navigation system.

(C.Lochert in based on the data forwarding method in geographical position, H.Hartenstein, J.Tian, H.Fuessler, D.Hermann, and M.Mauve, " A routing strategy for vehicular ad hoc networks in cityenvironments; " in In Proceedings of the IEEE Intelligent Vehicles Symposium, 2003, pp.156-161.), data forwarding can be divided into two stages usually, i.e. data transfer phae and forwarding recovery stage.In data transfer phae, the gross vehicle that carries data is that data forwarding is given from other nearer vehicle of base station distance, along with the forwarded hop-by-hop of data, finally forwards the data to the base station; If vehicle periphery then enters and transmits the recovery stage not than own other vehicle nearer apart from the base station, at this moment, vehicle carries out data forwarding according to neighbours in certain policy selection communication radius, for example selects at random.This method is applicable to the densely distributed situation of node in the network, and under the large-scale city environment, the situation that the vehicle distribution is comparatively sparse also is not suitable for.

Data forwarding method based on onboard navigation system comprises (I.Leontiadis and C.Mascolo such as Geopps, " Geopps:Geographical opportunistic routing for vehicular networks; " in World of Wireless, Mobile and Multimedia Networks, 2007.WoWMoM 2007.IEEE International Symposium on a, 2007, pp.1-6), these methods often suppose to have installed on the vehicle onboard navigation system, this system can plan mobile alignment for vehicle, and real-time traffic information is provided.According to the data forwarding of these information between can service vehicle, for example, the motion track of known each vehicle of hypothesis among the Geopps can be given the motion track vehicle nearer apart from base station location data forwarding when vehicle meets so.The strict navigation system that relies on of this method, have following defective: the traffic information that onboard navigation system provides often is difficult to accomplish in real time that accurately therefore, may there be mistake in the forwarding decision of making according to these information; At present, have only minority vehicle mounting onboard navigation system, require all vehicles the hypothesis of navigation system is installed and is false.

Summary of the invention

The objective of the invention is to overcome problems of the prior art, a kind of In-vehicle networking data forwarding method based on the prediction of vehicle motion track is provided.

Put it briefly, the inventive method comprises:

In a period of time, its track data when travelling of vehicle registration obtains a data set;

Afterwards, between the vehicle mutually near the time, calculate mobile trend separately, vehicle-mounted data is transmitted from high to low by mobile trend, finally is forwarded to the base station by the lower vehicle of mobile trend through the base station time;

Described mobile trend calculates by formula (4):

$T_i^{\left(M\right)}=\underset{m\∈[\mathrm{1,2},...,M]}{\min }\underset{j\∈S}{\mathrm{\Σ}}(p_{\mathrm{ij}}^{\left(m\right)}\·u_{S_j})---\left(4\right),$

Wherein, T _i ^(M)The mobile trend of expression vehicle; M represents the street transfer number set; S represents the street set; p _Ij ^(m)The expression vehicle is by the probability of current street i arrival another street j after the m step shifts, and this value is calculated based on described data set;

Represent the Euclidean distance between described another street and the nearest base station.

Describe said method below in detail.

The present invention regards city model as be made up of the street network structure, and there are two end points in each bar street, and end points is the crossing, as shown in Figure 1:

The crossing note is made I among the figure, and vehicle travels in the city, always drives to next crossing from a crossing, as the current I that is in of vehicle among the figure ₁The position, after travelling after a while, next crossing I ₂May be in I _{On 1}, I _{1 time}, I _{1 left side}, or I _{1 right side}In above-mentioned model, can regard the driving trace of vehicle as a string sequence of forming by the crossing.Part between two crossings is called a street, and note is made S.

Because people's daily life often has certain regularity, for example, someone drives to go to the job site through same or close route every day, perhaps goes home from the job site, therefore, can utilize the regularity that has on this mobile alignment to come forwarding of data between the service vehicle.

At first, vehicle is collected self driving trace data separately, and utilizes 2 rank Markov models to carry out modeling according to these data, obtains the prediction probability of vehicle motion track.Obtain following transition probability information after the modeling:

P(I _i+1|I _i，I _i-1) (1)

Formula (1) expression is according to the crossing I of preceding two processes of vehicle _iAnd I _I-1Predict next constantly the crossing of process be I _I+1Probability.In the modeling process, vehicle is at first collected the trace information that vehicle moves, and these information can utilize GPS and numerical map to obtain, the sequence of the information representation that obtains for forming by the crossing, for example: I ₁→ I ₂→ I ₃→ ....Data collection (as a week or one month) through after a while can obtain the vehicle driving trace data set, adds up the trace information of this data centralization, can obtain 2 rank markov transition probability information, i.e. formula (1) between all crossings.

Discuss according to the front, a street is two parts between the crossing.For convenience of description, note S _j=(I _j, I _J-1), why like this mark is because gross vehicle is to travel along the street, so the crossing of process also must be continuous, therefore, S _jCan represent I _jAn and last crossing I _J-1Between the street.It should be noted that two crossing I _iAnd I _I-1Between the street move order according to vehicle and be divided into (I _i, I _I-1) and (I _I-1, I _i) two kinds of situations, though both are identical from the geographical position, when transition probability calculated, because vehicle is to move in opposite direction, transition probability was inequality.Like this, the motion track of vehicle can be regarded as (S according to formula (1) by P _I+1| S _i) determine that 1 goes on foot the Markov chain of transition probability.P (the S that goes up directly perceived _I+1| S _i) represent that vehicle is by street S _iSail street S into _I+1Transition probability.The chapman-Kolmogorove equation of m step transition probability is by formula (2) expression, wherein p _Ij=P (S _i| S _j), and p _Ij ^(m)Expression S _iShift the back through the m step and arrive S _jProbability.Implication on this equation is directly perceived is that the multistep transition probability is decomposed, promptly between initial state i and done state j, introduce an intermediateness r (r represents a street of process in the transfer process from i to j), so just the step number m that multistep shifts can be reduced gradually, shift so that calculate through being reduced to a plurality of 1 steps after the iterative computation.

$P_{\mathrm{ij}}^{\left(m\right)}={\mathrm{\Σ}}_{r\∈S}(p_{\mathrm{ir}}^{\left(k\right)}\·p_{\mathrm{rj}}^{(m-k)})---\left(2\right)$

Wherein, k represents to shift step number, k=1, and 2 ... (m-1).

Suppose to have disposed in the city a plurality of base stations, so for certain street S _i, its value of utility

Represent its distance apart from the base station, owing to have a plurality of base stations, so this value of utility is defined as S _iAnd the Euclidean distance between the nearest base station, that is:

$u_{S_i}=\underset{j\∈[1,n]}{\min }\mathrm{dis}\tan \mathrm{ce}(S_i,B_j)---\left(3\right)$

According to formula (2) and (3), press the mobile trend of following formula 4 definition vehicles:

$T_i^{\left(M\right)}=\underset{m\∈[\mathrm{1,2},...,M]}{\min }\underset{j\∈S}{\mathrm{\Σ}}(p_{\mathrm{ij}}^{\left(m\right)}\·u_{S_j})---\left(4\right)$

Mobile trend T _i ^(M)From intuitively having predicted the street S of this vehicle from current place _iAfter M crossing (promptly shifting) through M time, the relative position relation between it and the base station, if vehicle drives towards the base station, then this value diminishes, if opposite vehicle sails out of the base station, then should the value increase.Vehicle may move towards the base station direction earlier in the process of moving, then again dorsad the base station move, then in whole process, the distance between vehicle and the base station diminishing earlier gradually, after become big gradually.Formula (4) only rounds the minimum range between the vehicle and base station in the process, as the mobile trend of vehicle.Fig. 2 is an example, and dotted line is represented the possible motion track of vehicle among the figure since vehicle A to compare vehicle B nearer apart from base station location from the motion track in future, the mobile trend of vehicle A is littler.

When (〉=2) more than two when vehicle meets, each vehicle counts above-mentioned mobile trend information according to self trace information, at first exchange mobile trend information between the vehicle, the vehicle that carries message compares the size of mobile trend, if it is bigger to carry the mobile trend of vehicle of data, then this vehicle forwards the data to the little vehicle of mobile trend, replacing former vehicle to carry data by it moves, further carry out data forwarding, until finally near the base station time, sending the data to the base station, transmit operation thereby finish data by a certain the less vehicle of mobile trend.

Although make vehicle-mounted data pass through forwarded hop-by-hop according to existing vehicle driving trace information by mobile trend mechanism from high to low above more and more near the base station, thereby but there is vehicle Iterim Change path or deviates from the situation that predicted value in the ideal can not realize data forwarding by the mechanism of setting because of other reasons, for certain compensation mechanism is provided, the present invention further is set as follows rule:

Within a certain period of time, fail to give another vehicle with data forwarding if be loaded with the vehicle of data, perhaps send the data to the base station, then this vehicle is transmitted to any vehicle near it at random with its data that are loaded with, in order to avoid data can not in time transmit, even the situation of loss of data appears.

Compare with prior art, advantage of the present invention is:

1, the present invention proposes the vehicle mobile trend prediction based on the historical data modeling, does not rely on specific onboard navigation system, has more general applicability;

2, the present invention carries out data forwarding based on the trend prediction that vehicle will move in future, and comparing existing prediction mode based on current location or moving direction has significant advantage.

Description of drawings

Fig. 1 represents the network structure schematic diagram that the street is formed;

Fig. 2 represents the mobile trend schematic diagram of vehicle;

Fig. 3 represents the data forwarding schematic flow sheet of the embodiment of the invention.

Embodiment

The invention will be further described in conjunction with the accompanying drawings below by a specific embodiment.

Present embodiment is according to the data forwarding in the realization of the data forwarding flow process shown in the accompanying drawing 3 In-vehicle networking.

1) vehicle A and B through one month driving trace data initial accumulated after, respectively obtain an initial data set, after this, this data set continuous updating.

2) suppose that vehicle A inside is provided with data acquisition unit, then the vehicle A data of carrying collection are travelled; Suppose that vehicle B is not provided with data acquisition unit, then vehicle B does not carry data and travels.Certainly the initial data that is used to transmit can be to come from data acquisition unit as mentioned above, also can be the data from other data sources, can according to circumstances specifically be provided with.

3) after vehicle A and vehicle B meet, calculate mobile trend T according to the present located street based on the above-mentioned data set that stores separately _i ^(M), exchange this mobile trend value then immediately.

4) the vehicle A mobile trend value of vehicle A and vehicle B relatively, if the mobile trend value of vehicle A greater than the mobile trend value of vehicle B, then vehicle A will carry data forwarding to vehicle B.

5) if 4) be false then vehicle A continues to carry message travels, wait for meeting next time, perhaps through the base station or satisfy other and when the condition of data is transmitted in the base station, directly forward the data to the base station.If fail in two hours to give other vehicles or base station, then automatically data forwarding is given after two hours any vehicle, to guarantee the timely transmission of data through vehicle A with data forwarding.

Claims (7)

1. In-vehicle networking data forwarding method based on vehicle mobile trend prediction comprises:

In a period of time, its track data when travelling of vehicle registration obtains a data set;

Afterwards, between the vehicle mutually near the time, calculate mobile trend separately, vehicle-mounted data is transmitted from high to low by mobile trend, finally is forwarded to the base station by the lower vehicle of mobile trend through the base station time;

Described mobile trend calculates by formula (4):

$T_i^{\left(M\right)}=\underset{m\∈[\mathrm{1,2},...,M]}{\min }\underset{j\∈S}{\mathrm{\Σ}}(p_{\mathrm{ij}}^{\left(m\right)}\·u_{S_j})---\left(4\right),$

Wherein, T _i ^(M)The mobile trend of expression vehicle; M represents the street transfer number set; S represents the street set; p _Ij ^(m)The expression vehicle is by the probability of current street i arrival another street j after the m step shifts, and this value is calculated based on described data set;

Represent the Euclidean distance between described another street and the nearest base station.

2. the In-vehicle networking data forwarding method based on vehicle mobile trend prediction as claimed in claim 1 is characterized in that, after described a period of time, described vehicle continue record its when travelling track data and upgrade described data set with this.

3. the In-vehicle networking data forwarding method based on vehicle mobile trend prediction as claimed in claim 1 is characterized in that, described track data described data centralization with vehicle sail through the mode of crossing sequence store.

4. as any described In-vehicle networking data forwarding method of claim 1-3, it is characterized in that the length of described a period of time is a week or January based on the prediction of vehicle mobile trend.

5. the In-vehicle networking data forwarding method based on the prediction of vehicle mobile trend as claimed in claim 1 is characterized in that M=3.

6. the In-vehicle networking data forwarding method based on the prediction of vehicle mobile trend as claimed in claim 1 is characterized in that p _Ij ^(m)Calculate by formula (2):

$p_{\mathrm{ij}}^{\left(m\right)}=\underset{r\∈S}{\mathrm{\Σ}}{p}_{\mathrm{ir}}^{\left(k\right)}\·p_{\mathrm{rj}}^{(m-k)}---\left(2\right)$

Wherein, k represents to shift step number, k=1, and 2 ... (m-1), r represents a street of process in the transfer process from i to j.

7. the In-vehicle networking data forwarding method based on the prediction of vehicle mobile trend as claimed in claim 1, it is characterized in that, in setting-up time length, if being loaded with the vehicle of data fails to give another vehicle with data forwarding, or send the data to the base station, then this vehicle is transmitted to any vehicle near it at random with its data that are loaded with.

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