JP6477087B2 - Vehicle search system - Google Patents

Description

  The present invention relates to a vehicle search system, and more particularly to a vehicle search system for searching for vehicles satisfying a search condition.

2. Description of the Related Art Conventionally, search techniques for searching for vehicles that satisfy certain conditions from a database are known.
For example, there is known a technique for searching for past vehicle travel history data (Patent Document 1).

  In addition, there is known a technique of receiving destination information and route information of car navigation on the server side and displaying an appropriate advertisement on the car navigation based on the information (Patent Document 2).

  Further, there is a prior art regarding a prediction model of vehicle behavior, particularly a route (Patent Document 3, Non-Patent Document 1, and Non-Patent Document 2).

JP 2000-163690 A JP, 2010-102685, A JP, 2013-120526, A

Brian D. Ziebart, Andrew L. Maas, Anind K. Dey, and J. Andrew Bagnell, “Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior”, UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing John Krumm, “A Markov Model for Driver Turn Prediction”, SAE 2008 World Congress

  Here, since “searching for future vehicle behavior” is a complex problem involving uncertainty, it is difficult to realize, for example, in a conventional database system that handles past mobile object information as described in Patent Document 1 above. For example, in response to the query "list vehicles that are likely to pass road A in the future until time T", using the usual database system, "destinations and routes entered in car navigation system" It stores via communication in a database that manages the input to the car navigation system in real time, and searches for it. Such an idea can be found, for example, in the above-mentioned Patent Document 2. Such a technique is effective for a vehicle in which a driver inputs a destination into a car navigation system, but there is a problem that it is impossible to search because the routes of other vehicles are uncertain. .

  On the other hand, future vehicle behavior can be predicted using the "prediction model of vehicle behavior". As a prediction model for predicting the traveling direction or route of a vehicle, for example, there is one as described in Non-Patent Document 1 or Non-Patent Document 2 above. Let the prediction of the behavior of the vehicle be expressed as y = f (x). Here, y is a predicted value, x is the current situation, and f is a predicted model. Although it can be said that future vehicle behavior retrieval is simply a task of "listing vehicles satisfying a certain condition y", f (x) is calculated for all vehicles, and the result is used as a condition Listing the matching vehicles increases the computational load, causing problems such as delays or increased computational resources. This problem is because the normal prediction model focuses on the "forward" prediction, and the application of the "backward" prediction that the prediction result satisfies a certain condition is not assumed. In normal database technology, building the index speeds up the speed of search, but “searching for future vehicle behavior” is a very dynamic and uncertain subject, and it is constantly changing, so It is considered difficult to construct at present.

  In order to avoid the above problem of increase in computational resources, distributed processing on the vehicle side as shown in Patent Document 3 above can be considered. However, in the method of Patent Document 3, since the vehicle receives a request for processing, the calculation (prediction) is started, which causes a delay. Moreover, in the method of the said patent document 3, it is based on the view which requests | requires a distributed processing preferentially with respect to the vehicle which has an idle resource. However, considering the prediction of the vehicle behavior, another vehicle unrelated to that will be asked to predict the own vehicle. This means transmitting the history of the destination of the vehicle and the route to the unrelated other vehicle, and privacy problems may occur. In order to solve these problems, an approach different from that of Patent Document 3 is required.

  The present invention has been made in view of the above circumstances, and an object thereof is to provide a vehicle search system capable of suppressing a delay of vehicle search.

  A vehicle search system according to a first aspect of the present invention for achieving the above object is a vehicle search system including a plurality of in-vehicle devices provided in a plurality of vehicles and an information processing device, wherein the in-vehicle device is Vehicle information transmission means for transmitting vehicle information about a vehicle to the information processing apparatus, and vehicle behavior prediction means for repeatedly predicting a time series of the behavior of the vehicle until after a predetermined prediction time based on the vehicle information Whether or not the search condition is satisfied based on the time series of the behavior of the vehicle predicted by the vehicle behavior prediction means when the search condition regarding the position and time transmitted from the information processing apparatus is received Search information generation means for generating information representing the information, and information indicating whether the search condition is generated or not generated by the search information generation means is sent to the information processing apparatus Search information transmitting means, and the information processing device is configured to transmit the search information from each of the plurality of vehicles based on the input search condition and the vehicle information transmitted from each of the plurality of in-vehicle devices Search condition for transmitting the search condition to each of the on-vehicle devices of the vehicle selected as the candidate of the vehicle by the vehicle selection means for selecting the candidate of the vehicle satisfying the search condition and the vehicle selection means And a vehicle search unit configured to search for a vehicle that satisfies the search condition based on information indicating whether the search condition is transmitted from each of the in-vehicle devices.

  According to the on-vehicle apparatus of the first aspect of the invention, the vehicle information transmission means transmits the vehicle information on the vehicle to the information processing apparatus. Then, based on the vehicle information, the vehicle behavior prediction means repeatedly predicts the time series of the behavior of the vehicle until after a predetermined prediction time.

  Then, when the search information generation unit receives the search condition regarding the position and time transmitted from the information processing apparatus, the search condition is satisfied based on the time series of the behavior of the vehicle predicted by the vehicle behavior prediction unit. Generate information that indicates whether or not. Then, the search information transmission unit transmits, to the information processing apparatus, information indicating whether or not the search condition generated by the search information generation unit is satisfied.

  Further, according to the information processing apparatus of the first aspect of the invention, each of the plurality of vehicles is selected based on the search condition input by the vehicle selection means and the vehicle information transmitted from each of the plurality of in-vehicle devices. Select vehicle candidates that satisfy the search condition.

  Then, the search condition transmission means transmits the search condition to each of the on-vehicle devices of the vehicle selected as the candidate of the vehicle by the vehicle selection means. Then, the vehicle search unit searches for a vehicle that satisfies the search condition based on the information indicating whether the search condition is transmitted from each of the in-vehicle devices.

  In this way, the on-vehicle device repeatedly predicts the time series of the behavior of the vehicle until after a predetermined prediction time based on the vehicle information, and when the search condition transmitted from the information processing device is received, the prediction The information processing apparatus generates information indicating whether or not the search condition is satisfied based on the time series of the behavior of the vehicle, and transmits the information indicating whether or not the generated search condition is satisfied to the information processing apparatus. The vehicle candidate selected as the vehicle candidate is selected from each of the plurality of vehicles based on the input search condition and the vehicle information transmitted from each of the plurality of in-vehicle devices The search condition is transmitted to each of the on-vehicle devices, and the vehicle search delay is suppressed by searching for the vehicle based on the information indicating whether the search condition transmitted from each of the on-vehicle devices is satisfied. be able to.

  A vehicle search system according to a second aspect of the present invention is a vehicle search system including a plurality of in-vehicle devices provided in a plurality of vehicles and an information processing device, wherein the in-vehicle devices include vehicle information related to the vehicles Vehicle information transmitting means for transmitting information to the information processing apparatus; vehicle behavior predicting means for repeatedly predicting a time series of behavior of the vehicle until after a predetermined prediction time based on the vehicle information; Vehicle behavior information transmission means for transmitting the time series of the behavior of the vehicle predicted by the vehicle behavior prediction means to the information processing device when the prediction result transmission request transmitted from the device is received; The information processing apparatus is configured to receive each of the plurality of vehicles based on the input search condition regarding position and time and the vehicle information transmitted from each of the plurality of on-vehicle devices. Transmitting the prediction result transmission request to each of the on-vehicle devices of the vehicle selected as the candidate of the vehicle by the vehicle selection means for selecting the candidate of the vehicle satisfying the search condition and the vehicle selection means Information indicating whether or not the search condition is satisfied for each of the in-vehicle devices, based on the prediction result transmission request transmitting means and the time series of the behavior of the vehicle transmitted from each of the in-vehicle devices Search information generation means; and vehicle search means for searching for a vehicle satisfying the search condition based on the information indicating whether the search condition is generated or not.

  According to the on-vehicle apparatus of the second aspect of the invention, the vehicle information transmitting means transmits the vehicle information on the vehicle to the information processing apparatus. Then, based on the vehicle information, the vehicle behavior prediction means repeatedly predicts the time series of the behavior of the vehicle until after a predetermined prediction time.

  Then, when the vehicle behavior information transmission unit receives the prediction result transmission request transmitted from the information processing apparatus, the vehicle behavior information transmission unit transmits to the information processing apparatus a time series of the behavior of the vehicle predicted by the vehicle behavior prediction unit.

  Further, according to the information processing apparatus of the second invention, the plurality of vehicles are based on the search condition input regarding the position and time by the vehicle selection means, and the vehicle information transmitted from each of the plurality of in-vehicle devices. From each of the, the candidate of the vehicle which satisfy | fills a search condition is selected. Then, the prediction result transmission request transmission unit transmits the prediction result transmission request to each of the in-vehicle devices of the vehicle selected as the candidate of the vehicle by the vehicle selection unit.

  Then, based on the time series of the behavior of the vehicle transmitted from each of the in-vehicle devices, the search information generation means generates, for each of the in-vehicle devices, information indicating whether or not the search condition is satisfied. Then, the vehicle search unit searches for a vehicle that satisfies the search condition based on the information indicating whether the search condition is generated by the search information generation unit.

  As described above, the on-vehicle apparatus repeatedly predicts the time series of the behavior of the vehicle based on the vehicle information until after a predetermined prediction time, and receives the prediction result transmission request transmitted from the information processing apparatus. The information processing apparatus transmits the time series of the predicted behavior of the vehicle to the information processing apparatus, and the information processing apparatus transmits the plurality of vehicles based on the input search condition and the vehicle information transmitted from each of the plurality of in-vehicle apparatuses. Each of them selects a candidate vehicle that satisfies the search condition, transmits a prediction result transmission request to each of the in-vehicle devices of the vehicle selected as the candidate of the vehicle, and transmits the behavior of the vehicle transmitted from each of the in-vehicle devices. Delaying the vehicle search by calculating information indicating whether or not the search condition is satisfied for each of the in-vehicle devices based on the time series, and searching for the vehicle based on the information indicating whether or not the search condition is satisfied. Suppress It is possible.

The search condition according to the present invention is a search condition including a traveling position of a vehicle and a time interval,
The vehicle behavior prediction means repeatedly predicts, based on the vehicle information, the time series of the traveling position of the vehicle until a prediction time predetermined as the upper limit of the time interval included in the search condition, and the search information The generation unit is configured to generate the traveling position included in the search condition in the time section included in the search condition based on the time series of the traveling position of the vehicle predicted by the vehicle behavior prediction unit. It is possible to calculate information indicating whether to drive or not.

  The information indicating whether the search condition is satisfied according to the present invention may be a certainty factor indicating a degree of satisfying the search condition.

  In the present invention, instruction information for instructing the task to be executed by the vehicle is a task according to information indicating whether or not the search condition is satisfied for each of the searched vehicles. And task transmitting means for transmitting the instruction information generated by the task generating means to the in-vehicle apparatus of the vehicle, the in-vehicle apparatus including the information processing apparatus from the information processing apparatus The information processing apparatus may further include task processing means for executing the task based on the transmitted instruction information.

  In the present invention, the task generation unit may instruct, for each of the searched vehicles, the presence or absence of presentation of information or the type of presentation information according to information indicating whether the search condition is satisfied or not. Information may be generated, and the task processing means may execute a task of presenting information to an occupant of the vehicle based on the instruction information transmitted from the information processing apparatus.

  As described above, according to the first aspect of the invention, the in-vehicle device repeatedly predicts the time series of the behavior of the vehicle based on the vehicle information until after a predetermined prediction time, and transmits it from the information processing device When the search condition is received, information indicating whether the search condition is satisfied is generated based on the predicted time series of the behavior of the vehicle, and information indicating whether the generated search condition is satisfied is information The information processing apparatus transmits to the processing apparatus, and based on the input search conditions and the vehicle information transmitted from each of the plurality of in-vehicle apparatuses, the candidate of the vehicle satisfying the search condition is selected from each of the plurality of vehicles. The search condition is transmitted to each of the on-vehicle devices of the vehicle selected as the candidate of the vehicle, and the vehicle is searched based on the information indicating whether the search condition transmitted from each of the on-vehicle devices is satisfied. By vehicle search It is possible to suppress the extension, the effect is obtained that.

  Further, according to the second aspect of the invention, the in-vehicle device repeatedly predicts the time series of the behavior of the vehicle based on the vehicle information until after a predetermined prediction time, and transmits the prediction result transmitted from the information processing device. When the request is received, the time series of the predicted behavior of the vehicle is transmitted to the information processing device, and the information processing device is based on the input search condition and the vehicle information transmitted from each of the plurality of in-vehicle devices. Then, from each of a plurality of vehicles, a candidate of vehicles satisfying the search condition is selected, and a prediction result transmission request is transmitted to each of the in-vehicle devices of the vehicle selected as the candidate of vehicles, and transmitted from each of the in-vehicle devices Calculating information indicating whether or not the search condition is satisfied for each of the in-vehicle devices based on the time series of the behavior of the vehicle, and searching for the vehicle based on the information indicating whether or not the search condition is satisfied; Search by vehicle It is possible to suppress the delay, the effect is obtained that.

FIG. 1 is a block diagram showing a configuration of a vehicle search system according to a first embodiment of the present invention. It is an explanatory view for explaining route prediction of vehicles. It is explanatory drawing for demonstrating discretizing map data by mesh. It is a conceptual diagram for demonstrating hash table H. FIG. It is a conceptual diagram for demonstrating the update of the hash table H. FIG. It is a conceptual diagram for demonstrating auxiliary | assistant hash table G. FIG. It is a conceptual diagram for demonstrating the setting of the area | region Q. FIG. It is an explanatory view for explaining an operation example of a server of a vehicle search system. It is a flowchart which shows the content of the prediction processing routine in the vehicle-mounted apparatus which concerns on embodiment of this invention. It is a flowchart which shows the content of the vehicle behavior prediction process routine in the vehicle-mounted apparatus which concerns on embodiment of this invention. It is a flowchart which shows the content of the data storage process routine in the server which concerns on embodiment of this invention. It is a flowchart which shows the content of the search process routine in the server which concerns on embodiment of this invention. It is a flowchart which shows the content of the simple prediction process routine in the server which concerns on embodiment of this invention. It is a flowchart which shows the content of the inference processing routine in the vehicle-mounted apparatus which concerns on embodiment of this invention. It is a flowchart which shows the content of the reliability calculation process routine in the vehicle-mounted apparatus which concerns on embodiment of this invention. It is a block diagram showing composition of a vehicle search system concerning a 2nd embodiment of the present invention.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

First Embodiment
<Configuration of Vehicle Search System 210 According to First Embodiment>
As shown in FIG. 1, the vehicle search system 10 according to the first embodiment of the present invention includes a plurality of in-vehicle devices 12 provided in a plurality of vehicles and vehicle information transmitted from the plurality of in-vehicle devices 12. And a server 14 for receiving and searching for a vehicle satisfying the search condition. The plurality of in-vehicle devices 12 and the server 14 are connected via a network 16 such as the Internet. The server 14 is an example of an information processing apparatus.

In the present embodiment, the user asks the vehicle search system 10 a question (query) on the future vehicle behavior such as "a vehicle likely to pass the road segment A by T _q minutes and its certainty factor". Inquire Then, the in-vehicle device 12 and the server 14 of the vehicle search system 10 output the search result for the query. Hereinafter, conditions the behavior of the vehicle "are likely to pass through the road segment A until after T _q partial vehicle" is explained as an example the case of using as a search condition relating to the position and the time interval.

[In-vehicle device 12]
The on-vehicle device 12 measures the position of the vehicle one after another, the vehicle sensor 124 sequentially detects the speed of the vehicle, the vehicle position information measured by the vehicle position measuring unit 120, and the vehicle sensor And a computer 126 for predicting the behavior of the host vehicle based on the speed of the host vehicle detected by 124. The in-vehicle device 12 is mounted on a vehicle.

  The position measurement unit 120 is configured using, for example, a GPS sensor, and measures position information (X, Y) at the current time t of the host vehicle. The position information includes the latitude X and the longitude Y at which the vehicle is located at the current time t.

  The computer 126 includes a CPU, a RAM, and a ROM storing programs for executing processing routines to be described later, and is functionally configured as follows. The computer 126 is an information acquisition unit 128 for acquiring the position information (X, Y) of the current time t of the host vehicle measured by the position measurement unit 120 and the speed of the host vehicle detected by the vehicle sensor 124 A communication unit 130 that sequentially transmits the vehicle information acquired by the acquisition unit 128 to the server 14 together with the vehicle ID, and transmits and receives data to and from the server 14, and position information of the own vehicle acquired by the information acquisition unit 128 And the vehicle behavior prediction unit 132 that predicts the predicted route of the own vehicle based on the speed of the own vehicle, and the search condition transmitted from the server 14 generates information indicating whether or not the search condition is satisfied. And a task processing unit 136 for executing the task when the task instruction information transmitted from the server 14 is received. The communication unit 130 is an example of a vehicle information transmission unit and a search information transmission unit. The inference unit 134 is an example of a search information generation unit.

  The information acquisition unit 128 acquires the position information (X, Y) of the current time t of the own vehicle measured by the position measurement unit 120 as vehicle information on the vehicle. Further, the information acquisition unit 128 acquires the speed of the host vehicle at the current time t detected by the vehicle sensor 124.

  The communication unit 130 sequentially transmits the position information (X, Y) of the current time t of the vehicle acquired by the information acquisition unit 128 to the server 14. In addition, the communication unit 130 receives the search condition related to the position and the time interval transmitted from the server 14. When the communication unit 130 receives the search condition transmitted from the server 14, the communication unit 130 transmits, to the server 14, information indicating whether the search condition generated by the inference unit 134 described later is satisfied. The communication unit 130 also receives the instruction information transmitted from the server 14 and instructing the task to be performed by the vehicle.

The vehicle behavior prediction unit 132 uses the position information (X, Y) of the current time t of the host vehicle acquired by the information acquisition unit 128 and the speed of the host vehicle to the prediction limit determined in advance. Repeatedly predict the prediction path of The predicted route of the host vehicle is an example of a time series of the behavior of the host vehicle.
Even when the search condition is not received from the server 14, the vehicle behavior prediction unit 132 sequentially updates the prediction, for example, every several milliseconds to several seconds to several minutes. By sequentially updating the prediction of the behavior of the vehicle, it is possible to respond faster than when performing prediction after receiving the search conditions, and it is possible to alleviate the problem of delay. In addition, it is possible to smooth the load by sequentially predicting the route of the vehicle and scheduling so as to be performed when there is enough computing resource.

  In the present embodiment, the case where the route on which the host vehicle travels is predicted by T minutes (for example, about 1 to 10 minutes) will be described as an example (hereinafter, T is referred to as a prediction limit). The prediction limit T is predetermined and corresponds to the upper limit of the time interval included in the search condition.

FIG. 2 is a diagram for explaining a route prediction model for predicting the route of the host vehicle.
As shown in FIG. 2, at time t, it is assumed that the history of the route traveled by the vehicle (a list of road segment IDs) is r _t-1 , r _t-2 , ..., r _t-N. Do. At this time, the road segment at the next time can be predicted based on the conditional probability p (r _t | r _t−1 , r _t−2 ,..., R _t−N ) estimated in advance. The conditional probability is obtained by statistically learning from the travel history of the individual or the travel history of all the vehicles. The conditional probability is a road segment connected to the road segment A when the host vehicle is currently traveling on the road segment A, and the probability that any of the road segments the host vehicle will travel next has a high probability It indicates whether there is a certain condition, and models “a straight advance is greater in probability than a right turn,” and “the probability that two consecutive right turns occur is low” or the like.
In addition, when a destination is input to the car navigation system, it is possible to reflect that the possibility of using the route presented by the car navigation system is high.

In the present embodiment, the case of predicting a path by sampling will be described as an example. That is, the vehicle behavior prediction unit 132 selects one road segment traveling next with a probability proportional to the conditional probability p (r _t | r _t−1 , r _t−2 ,..., R _t−N ). To sample. _R = the history of the path _{{r t-1, r t} -2, ···, r t-N} and represents an operation for obtaining a _{r t} is sampled at the time of history _{R r t} ← S (R) It represents. Further, if the average required travel time of the r _t to be expressed as T (r _t), it can be predicted with the procedure as shown in the following pseudocode. Incidentally, the average required travel time T (r _t) of the r _t is calculated by including the speed limit of the history or road segment speed of the vehicle.

[Pseudo code]
(Algorithm / path sampling)
Input: Path history R = {r _{t -1} , r _t -2, ..., r _t-N }, prediction limit T
Output: predicted route _{R '= {r t + K} , r t + K-1, ···, r t}

R '{{} // empty array T ₀ 0 0 // end when prediction limit T is exceeded while T ₀ <T
r S S (R '∪ R) // Sampling from the past travel history R' R R '∪ {r} // Add to predicted route T ₀ T T ₀ + T (r) // Required travel time T End while adding (r)
return R '
[End of pseudo code]

  The above algorithm samples one prediction path up to T minutes after the prediction limit. By executing this algorithm a sufficient number of times, multiple predicted paths are obtained. The list of predicted paths can be regarded as probabilistic path prediction approximately up to T minutes later. In this embodiment, the above prediction algorithm is executed J times to obtain J prediction paths.

  When the inference unit 134 receives the search condition transmitted from the server 14, the inference unit 134 searches based on the predicted route of the own vehicle predicted by the vehicle behavior prediction unit 132 as information indicating whether or not the search condition is satisfied. A certainty factor representing the degree of satisfying the condition is calculated. The search conditions include the traveling position of the vehicle and the time section.

Specifically, the inference unit 134 converts the predicted route of the vehicle into a format required by the server 14. The format after conversion depends on the search condition of the server 14, but in the following, "enumerate vehicles passing the road segment A with a certainty P or more within T _q minutes" on the server 14 side (however, T _q < The case where it is a query called T; T is the prediction limit of a vehicle is demonstrated.

By processing the list of predicted routes predicted by the vehicle behavior prediction unit 132 using the following algorithm, “the certainty factor of passing the road segment A within T _q minutes” is calculated.

[Pseudo code]
[Calculation of certainty of passing road segment in algorithm / inference unit 134]
Input: list of predicted routes L = {R ₁ , R ₂ ,..., R _K }, road segment A, time T _q
Output: Certainty P that the vehicle passes road segment A within T _q minutes
P <−0 // confidence factor K <−length (L) // number of predicted paths For each (R in L): // included in predicted path list L
If the loop T _c <−0 // T _c > T _q for all paths R, then this path R
A is considered not included For each (r in R): // included in route R
Loop for road segment r
if (r == A): // When A is included in route R
P <-P + 1 / K /// Increase confidence
break this inner loop
// Since processing of route R is over
Consider the next route R '
end if
T _c <−T _c + T (r) /// time of r t minutes
Only increase
if (T _c > T _q ): // Exit when exceeding T _q
break this inner loop
end if
end inner for loop
end outer for loop
return P
[End of pseudo code]

Since the calculation of the certainty factor depends on the road segment A and the time T _q and is unknown until the search condition is received, it can not be calculated in advance.

Although the above calculation of the certainty factor has been described by way of example in which the query is "enumerate vehicles passing through road segment A with a certainty factor P or more within T _q minutes", other types of queries are also described. The certainty factor can be calculated by an algorithm of the same idea basically as described above. Further, since the vehicle behavior prediction process is a process common to all types of queries, it is not necessary to perform another type of vehicle behavior prediction process to increase the types of queries. This is because sequential vehicle behavior prediction processing and on-demand inference processing (certainty factor calculation processing) are separated. In addition, when a new type of query is to be implemented, it is also conceivable to transmit and execute a new inference processing program to the vehicle side.

  In addition, it is conceivable that there is no idle resource on the in-vehicle device 12 side of the vehicle, and therefore the route of the vehicle, which should normally be updated sequentially on the vehicle side, may become outdated. In this case, the server 14 side is notified of that fact, and the server 14 side may receive it and decide whether to perform the prediction and inference of the route on the server 14 side in consideration of the balance with the calculation resource and the like.

  The task processing unit 136 executes the task based on the task instruction information transmitted from the server 14. In the present embodiment, the task processing unit 136 executes a task of presenting information to the occupant of the vehicle based on the instruction information transmitted from the server 14 as an example. For example, the task processing unit 136 displays an advertisement on the display unit of the car navigation system when stopping at a signal.

[Server 14]
The server 14 is configured by a server including a CPU, a ROM storing programs for realizing processing routines to be described later, a RAM for temporarily storing data, a memory as storage means, a network interface, etc. Functionally, it can be expressed by a configuration including the reception unit 140, the communication unit 142, the data storage unit 144, the database 145, the simple prediction unit 146, the vehicle search unit 148, and the task generation unit 150. The communication unit 142 is an example of a search condition transmission unit and a task transmission unit, and the simple prediction unit 146 is an example of a vehicle selection unit.

The receiving unit 140 receives the query input to the server 14. The query is, for example, "a question on future vehicle behavior" such as "a vehicle likely to pass through the road segment A by T _q minutes and its certainty factor". As described above, the search conditions included in the query include the traveling position of the vehicle and the time interval. In the above, we considered the query “vehicle likely to pass through road segment A by T _q minutes”, but the search condition in this embodiment is as follows for various temporal and spatial conditions of vehicle position: Can be considered.

(1) The road segment A passes within ₁ minute T, then the road segment B vehicles (2) which passes within ₂ minutes T passing through the road segment A within ₁ minute T, then the road segment B T ₂ vehicle turning right road segment a non vehicle (3) the vehicle (4) to pass through the road segment a or road segment B within _one minute T T within _one minute passes within a minute, and the like.

  The communication unit 142 receives vehicle position information (X, Y) transmitted from each of the plurality of on-vehicle devices 12. In addition, the communication unit 142 transmits the search condition included in the query received by the reception unit 140 to each of the on-vehicle devices 12 of the vehicles selected as candidates for the vehicle by the simple prediction unit 146 described later. Further, the communication unit 142 receives the certainty factor transmitted from each of the in-vehicle devices 12 having received the search condition. Further, the communication unit 142 transmits instruction information generated by the task generation unit 150 described later to the on-vehicle device 12 of the vehicle.

  The data storage unit 144 stores, in the database 145, a combination of the vehicle position information (X, Y) transmitted from each of the plurality of in-vehicle devices 12 received by the communication unit 142 and the vehicle ID of the vehicle.

  Specifically, the data storage unit 144 acquires (vehicle ID, X (latitude), Y (longitude)) received by the communication unit 142. In the present embodiment, as shown in FIG. 3, the latitude and longitude on the map are discretized into meshes, and a mesh ID is assigned to each of the discretized meshes. Hereinafter, the vehicle ID is represented by V.

The data storage unit 144 stores (vehicle ID, X (latitude), Y (longitude)) received by the communication unit 142 in a hash table H (data storage location) having the mesh ID as a key. Assuming that the mesh ID is m, all vehicle data included in the mesh m are stored in H [m] as a “hash table using the vehicle ID as a key”. That is, the structure of the hash table H is realized as a double hash table as shown in FIG. Here, m _n represents a mesh ID, V _n represents a vehicle ID, and (X _n , Y _n ) represent latitude and longitude.

Further, due to the restriction that only the latest data received by the communication unit 142 is stored in H, when storing a vehicle of the vehicle ID (V), as shown in FIG. It is necessary to delete it from the hash table H. Therefore, when a vehicle ID is given, it is necessary to specify in which mesh ID the data is stored. Therefore, as shown in FIG. 6, an auxiliary hash table G is prepared which enables search of vehicle ID → mesh ID. In the hash table of FIG. 4 above, it can be seen that V ₁ belongs to m ₁ , but because of the nature of the hash table, the reverse lookup (in the direction opposite to the arrow) search is extremely inefficient. The correspondence from the vehicle ID to the mesh ID can be efficiently performed using the table G. By using the auxiliary hash table G, as shown in FIG. 5 described above, it can be understood that V ₂ belongs to m ₁ , so that data of V ₂ can be erased from H [m ₁ ].

  Summarizing the above, data storage processing is as shown in the following pseudo code.

[Pseudo code]
[Algorithm / data storage]
Input: (Vehicle ID, latitude, longitude) (hereinafter referred to as (V, X, Y).)
oldMeshID <-G [v] // The current status of the vehicle ID (V) data
Remove the data of a specific vehicle V from H [oldMesh] to which mesh it is included // Figure 5
newMeshID <-Discretize (X, Y) / / Calculate mesh ID
(Figure 3)
H [newMeshID] [V] <-(X, Y) // Mesh newMeshID
New vehicle IDV to drive
Data stored G [V] <-newMeshID // Auxiliary hash table updated [pseudo code end]

  The database 145 stores (vehicle ID, X (latitude), Y (longitude)) for each of the vehicles. Also, in the database 145, each data is stored by the hash table H and the auxiliary hash table G.

  The simple prediction unit 146 searches each of the plurality of vehicles based on the search condition included in the query received by the reception unit 140 and the position information (X, Y) of the plurality of vehicles stored in the database 145. Select candidate vehicles that satisfy the conditions.

  It is a waste of vehicle resources to issue a command “calculate the certainty factor satisfying the search condition” to the on-vehicle devices 12 of all the vehicles. A more efficient calculation can be achieved by using a simple forecast that takes physical constraints into consideration, without strictly predicting it. For example, when searching for a vehicle whose vehicle position after 5 minutes is in the vicinity of point A, assuming that the vehicle is 120 km / h or less per hour, only for vehicles that are present within 10 km from point A at present. It is sufficient to calculate the degree of certainty.

  Therefore, the requirement for the simple prediction unit 146 is high-speed using the information of the position information (X, Y) of a plurality of vehicles stored in the database 145 as the vehicle which may match the search condition ( It can be extracted in real time).

  Specifically, first, using the hash table H of the database 145, the simple prediction unit 146 uses the hash table H of the database 145 (in the example of "vehicles within 10 km from the road segment A described above, The area Q is a 10 km circle centered on the road segment A, and the vehicle ID running on the list is listed. The figure for demonstrating the relationship between the area | region Q and a mesh in FIG. 7 is shown. In FIG. 7, the fine hatched portion represents the region Q, and the coarse hatched portion represents the mesh set including the region Q. As shown in FIG. 7, when a region Q is given, we want to obtain a mesh set (as few as possible) that includes the region Q. This selects one point in the area Q appropriately, converts it to a mesh ID, searches the mesh around that mesh, and adds it as a mesh set if there is a common part with the area Q It can be obtained by repeating things. When the region Q is searched, it suffices to search the region Q and the mesh set M corresponding to the coarse hatched portion, but in the present embodiment, the vehicle in the region Q is searched without excess or deficiency. Then, the vehicles present at positions not included in the area Q are removed from the rough hatched portion.

  Once the mesh set M corresponding to the area Q is obtained, H [m] is listed for all its element meshes m, and whether each vehicle included in H [m] is really included in the area Q By checking, the purpose of listing up the vehicle IDs traveling in the area Q is achieved.

  Summarizing the above, search of the database is as shown in the following pseudo code.

[Pseudo code]
[Algorithm / Simplified Prediction]
Input: area Q
Output: List W of vehicles traveling in area Q
W <-{}
Acquisition of mesh set including M <−Q For each (m in M):
For each (v in H [m]):
(X, Y) <-H [m] [V]
if ((X, Y) is included in Q):
W <-W∪ {V}
end if
end inner for loop
End outer for loop
[Pseudo code end]

The vehicle search unit 148 searches for a vehicle that satisfies the search condition based on the degree of certainty transmitted from each of the in-vehicle devices 12. Specifically, the vehicle search unit 148 searches for a vehicle that satisfies the search condition based on the information indicating the combination of the vehicle ID and the certainty factor received by the communication unit 142. For example, when the search condition is “a vehicle that is likely to pass the road segment A after T _q minutes”, the vehicle search unit 148 sets a vehicle with a certainty factor of 50% or more as a vehicle that satisfies the search condition. Also, if the search query is, for example, "enumerate vehicles passing through the road segment A with a certainty P or more within T _q minutes", the vehicle search unit 148 selects vehicles with a certainty P or more. It is assumed that the vehicle satisfies the search condition.

For the processing of the communication unit 142, the simple prediction unit 146, and the vehicle search unit 148, as shown in FIG. 8, the query "list vehicles that are likely to pass through the road segment A by T _q minutes and their certainty factors" Will be described in terms of how the server 14 returns results.

(1) First, an area where a vehicle which can pass through the road segment A by T _q minutes at present can be determined by considering the physical speed of the vehicle. This is the area Q. By performing area query processing in the simple prediction unit 146 with the area Q as an input, vehicles traveling in the current area Q are listed.
The communication unit 142 transmits a query “notify the certainty of passing the road segment A by T _q minutes” to the in-vehicle device 12 to all the vehicles traveling the listed Q.

(2) If the calculation result of the certainty factor of all the vehicles is returned on the server 14 side, this is taken as the result, but even if the result is not uniform within the predetermined time, only the returned one is used as the result I assume. Also, in some cases, it is possible to obtain a list of vehicles with a high degree of certainty by removing ones having a certainty or less in the list of results.

  The task generation unit 150 generates, based on the information representing the combination of the vehicle ID and the certainty factor received by the communication unit 142, instruction information which is a task according to the certainty factor and which instructs the vehicle to execute the task. Do. In the present embodiment, an example will be described in which task generation unit 150 generates instruction information for instructing the presence or absence of information or the type of presentation information according to the degree of certainty.

  In the above-mentioned FIG. 8 (3), an advertisement is mentioned as an example of a concrete task. Informing vehicles that are likely to pass a certain road segment, sales information of stores along the street, etc. enables more targeted and appropriate advertising. In this example, the presence or the type of the advertisement is determined according to the certainty factor P. This strategy may be performed according to a pre-defined database.

<Operation of vehicle search system>
Next, the operation of the vehicle search system 10 according to the embodiment of the present invention will be described. When each of the vehicles mounted with the in-vehicle apparatus 12 is traveling, the in-vehicle apparatus 12 of each vehicle executes a prediction process routine shown in FIG. 9, and the server 14 executes a search process routine shown in FIG.

  First, the prediction processing routine executed by the in-vehicle device 12 of each vehicle will be described. In the on-vehicle apparatus 12, when the position measurement unit 120 measures the position information (X, Y) of the current time t of the vehicle and the vehicle sensor 124 detects the speed of the vehicle at the current time t, FIG. Execute the prediction processing routine shown in

<Prediction processing routine>
First, in step S100, the information acquisition unit 128 acquires, as vehicle information, the position information (X, Y) of the current time t of the host vehicle measured by the position measurement unit 120. Further, the information acquisition unit 128 acquires the speed of the host vehicle at the current time t detected by the vehicle sensor 124.

  In step S102, the communication unit 130 transmits the position information (X, Y) of the current time t of the host vehicle acquired in step S100 and the vehicle ID to the server 14.

  Then, in step S104, the vehicle behavior prediction unit 132 performs prediction based on the position information (X, Y) of the current time t of the host vehicle acquired in step S100 and the speed of the host vehicle. The predicted route of the vehicle is predicted until after time. Step S104 is realized by a vehicle behavior prediction process routine shown in FIG.

<Vehicle behavior prediction processing routine>
In step S200, based on the time series of the position information (X, Y) of the vehicle acquired in step S100, the history R of the route traveled by the vehicle is acquired.

  In step S201, 1 is substituted into a variable j related to the number of predicted paths, thereby initializing.

In step S202, the predicted route R ′ and the time variable T ₀ are initialized.

In step S 204, it is determined whether the time variable T ₀ is smaller than the prediction limit T. When the time variable _{T 0} is less than the predicted limit T, the process proceeds to step S206. On the other hand, the time variable _{T 0} is the case of the above prediction limit T, the process proceeds to step S211.

In step S206, the history R of obtained path in step S200, a route R 'predicted in the step S206 up to the previous time, probabilities estimated in advance conditions _{p (r t | r t-} 1, r _{Based on t−2} ,..., r _t−N ), the route r predicted to be traveled by the host vehicle at the next time is sampled.

  In step S208, the route r sampled in step S206 is added to the predicted route R '.

In step S210, the added r average required travel time T a (r) in the above step S208, is added to the time variable _{T 0.}

  In step S211, the predicted route R 'obtained in steps S204 to S210 is stored in a memory (not shown).

  In step S212, it is determined whether the number J of predetermined prediction paths is equal to a variable j corresponding to the number of prediction paths R 'stored in the memory in step S211. If the number J of prediction paths is equal to the variable j, the process proceeds to step S216. On the other hand, if the number J of prediction paths is different from the variable j, the variable j is incremented in step S214, and the process returns to step S202.

  In step S216, the plurality of predicted routes stored in the memory in step S211 are output as a result, and the vehicle behavior prediction processing routine is ended.

Next, the search processing routine executed by the server 14 will be described. When the server 14 receives the vehicle ID and the position information (X, Y) of the vehicle transmitted from at least one on-vehicle device 12, the server 14 executes a data storage processing routine shown in FIG.
Also, when the server 14 receives a query input from the user, the server 14 executes a search processing routine shown in FIG.

<Data storage processing routine>
In step S300, the received vehicle ID and position information (X, Y) of the vehicle are received.

  In step S302, using the auxiliary hash table G, the mesh ID to which the vehicle ID currently belongs is specified from the vehicle ID received in step S300.

  In step S304, the data corresponding to the vehicle ID accepted in step S300 is removed from the hash table corresponding to the mesh ID identified in step S302.

  In step S306, a new mesh ID is calculated based on the vehicle position information (X, Y) acquired in step S300.

  In step S308, the data corresponding to the vehicle ID received in step S300 is stored in the hash table corresponding to the mesh ID calculated in step S306.

  In step S310, the auxiliary hash table G is updated based on the new mesh ID calculated in step S306, and the data storage processing routine is ended.

<Search processing routine>
First, in step S400, the receiving unit 140 receives the query input to the server 14.

  Next, in step S402, the simple prediction unit 146 searches the search conditions included in the query received in step S400, and the position information (X, Y) of a plurality of vehicles stored in the database 145 in the data storage processing routine. And select a candidate vehicle that satisfies the search condition from each of the plurality of vehicles. Step S402 is realized by the simple prediction process routine shown in FIG.

<Simple prediction process routine>
In step S500, an area Q having a range defined in advance based on the position (road segment) included in the search condition according to the time section included in the search condition is received.

  In step S502, a list W of vehicles is initialized.

  In step S504, a mesh set including the area Q received in step S500 is acquired.

  In step S506, one mesh m is set from the mesh set acquired in step S504.

  In step S508, based on each of the position information (X, Y) stored in the database 145, one vehicle ID of each of the vehicle IDs included in the mesh m set in step S506 is set.

  In step S510, from each of the position information (X, Y) stored in the database 145, the position information (X, Y) of the vehicle corresponding to the vehicle ID set in step S508 is acquired.

  In step S512, based on the position information (X, Y) of the vehicle acquired in step S510, whether the latitude X and longitude Y at which the vehicle is located is included in the area Q acquired in step S500 Determine If the latitude Q and the longitude Y at which the vehicle is located are included in the area Q, the process proceeds to step S514. On the other hand, if the latitude X and the longitude Y at which the vehicle is located are not included in the area Q acquired at step S500, the process proceeds to step S516.

  In step S514, the vehicle ID set in step S508 is stored in the list W of vehicles.

  In step S516, it is determined whether the process of step S508-step S514 was performed about all the vehicles which belong to the mesh m set by the said step S506. If the processes of steps S508 to S514 have been executed for all the vehicles belonging to the mesh m set in step S506, the process proceeds to step S518. On the other hand, if there is a vehicle that has not performed the processing of steps S508 to S514 for the vehicle belonging to the mesh m set in step S506, the process returns to step S508.

  In step S518, it is determined whether the processing in steps S506 to S516 has been performed for all meshes included in the mesh set acquired in step S504. If the processes of steps S506 to S516 have been performed for all meshes included in the mesh set acquired in step S504, the process proceeds to step S520. On the other hand, if there is a mesh that has not been subjected to the processes of steps S506 to S516 for the meshes included in the mesh set acquired at step S504, the process returns to step S506.

  In step S520, each of the vehicle IDs stored in the list W of vehicles in step S514 is output as a candidate of the vehicle satisfying the search condition as a result, and the simplified prediction processing routine is ended.

  Next, the process returns to the search processing routine, and in step S404, the search condition included in the query received in step S400 is transmitted to each of the in-vehicle devices 12 of the vehicle selected as the candidate of the vehicle in step S402.

  Here, when each of the in-vehicle devices 12 of the vehicle selected as the candidate of the vehicle receives the search condition transmitted from the server 14 in step S404 by the communication unit 130, the inference unit 134 of the in-vehicle device 12 Execute the inference processing routine shown in. Hereinafter, after the inference processing routine executed in the in-vehicle apparatus 12 is described, the processing after step S406 of the search processing routine executed by the server 14 will be described.

<Inference processing routine>
In step S600, the plurality of predicted paths output in step S104 of the prediction processing routine and the search condition received by the communication unit 130 are acquired.

  In step S602, the inference unit 134 calculates the certainty factor based on the plurality of predicted routes and the search conditions of the vehicle acquired in step S600. Step S602 is realized by the certainty factor calculation process routine shown in FIG.

<Confirmation factor calculation processing routine>
In step S700, the time T _q and the road segment A included in the search condition acquired in step S600 are acquired.

  In step S702, the certainty factor P is initialized.

  In step S704, the number K of predicted routes acquired in step S600 is acquired.

  In step S706, one prediction path R is set among the plurality of prediction paths acquired in step S600.

In step S 708, a time variable T _c is initialized.

  In step S710, one road segment r is set among the road segments included in the predicted route R set in step S706.

  In step S712, it is determined whether the road segment r set in step S710 matches the road segment A acquired in step S700. If the road segment r matches the road segment A, the process proceeds to step S713. On the other hand, if the road segment r does not match the road segment A, the process proceeds to step S714.

  In step S713, the certainty factor P initialized in step S702 or the certainty factor P updated in the previous main step S713 is increased according to the increasing equation PPP + 1 / K.

In step S714, the initialization time variable _{T c} or previous time variable _{T c} updated in the step S714 in the above step S 708, the average required travel time of a road segment r set in step S710 T Add (r _t ).

In step S716, it is determined whether the time variable T _c updated in step S714 is larger than the time T _q acquired in step S700. If the time variable T _c is larger than the time T _q , the process returns to step S706. On the other hand, if the time variable _{T c} is equal to or less than the time _{T q,} the process proceeds to step S718.

  In step S718, it is determined whether the process of step S710 to step S716 has been performed for all the road segments included in the predicted route R set in step S706. When the process of step S710 to step S716 is performed for all road segments included in the predicted route R set in step S706, the process proceeds to step S720. On the other hand, if there is a road segment for which the process of steps S710 to S716 has not been performed for the road segment included in the predicted route R set in step S706, the process returns to step S710.

  In step S720, it is determined whether the process of step S706 to step S718 is performed for all of the K predicted paths acquired in step S704. If the processes of steps S706 to S718 have been executed for all of the K predicted paths obtained in step S704, the process proceeds to step S722. On the other hand, if there is a predicted route for which the process of steps S706 to S718 has not been performed for the K predicted routes obtained in step S704, the process returns to step S706.

  In step S722, the certainty factor P updated in step S713 is output as a result, and the certainty factor calculation processing routine is ended.

  Next, the process returns to the inference processing routine, and in step S604, the communication unit 130 transmits the certainty factor P output in step S602 to the server 14, and the inference processing routine is ended.

  Next, processing after step S406 of the search processing routine executed by the server 14 will be described.

  In step S406, the communication unit 142 of the server 14 receives the certainty factor P transmitted from each of the in-vehicle devices 12.

  In step S408, it is determined whether the certainty factor P has been received from all the vehicles for which the search conditions have been transmitted in step S404. When the certainty factor P is received from all the vehicles which transmitted the search condition, the process proceeds to step S410. On the other hand, when the certainty factor P is not received from all the vehicles which transmitted the search condition, it returns to step S406. In addition, even if the certainty factor P is not received from all of the vehicles that have transmitted the search condition, when a predetermined time has elapsed, the process proceeds to step S410.

  In step S410, the vehicle search unit 148 searches for a vehicle that satisfies the search condition included in the query based on each of the certainty factors P received in step S406.

  In step S412, instruction information is generated for each of the vehicles searched in step S410, which is a task corresponding to the degree of certainty transmitted from the vehicle and which instructs the vehicle to execute the task.

  In step S414, each of the instruction information for instructing the task to be executed by the vehicle generated in step S412 is transmitted to each of the vehicles, and the search processing routine is ended.

  In each of the in-vehicle devices 12 having received the instruction information, the task processing unit 136 executes a task based on the instruction information received by the communication unit 130.

  As described above, according to the vehicle search system of the first embodiment of the present invention, the in-vehicle device repeatedly predicts the predicted route of the vehicle after the predetermined prediction time based on the vehicle information. When the search condition transmitted from the server is received, the certainty factor is generated based on the predicted route of the predicted vehicle, and the generated certainty factor is transmitted to the server, and the server transmits the certainty factor with the inputted search condition. Based on the vehicle information transmitted from each of the plurality of in-vehicle devices, a candidate of vehicles satisfying the search condition is selected from each of the plurality of vehicles, and for each of the in-vehicle devices of the vehicle selected as a candidate of the vehicle By transmitting the search condition and searching for the vehicle based on the certainty factor transmitted from each of the in-vehicle devices, it is possible to suppress the delay of the vehicle search.

  In addition, in order to predict the future position sequentially, the result can be returned if only inference processing (certainty factor calculation processing) is performed at the stage where the server side makes an inquiry. Can be shortened, and complex search processing with uncertainty of “search for future vehicle position” can be realized with a large number of candidate vehicles while suppressing delay.

  In addition, since the server side does not perform all processing, and the vehicle side resources are used in a distributed manner, a small load on the server side can be realized, and even if the number of vehicles increases, it is not necessary to increase server side resources. You can search for vehicles.

  In addition, by reducing the number of vehicles that should perform inference processing, the amount of communication between the vehicle and the server and the average calculation load per vehicle can be reduced, and the total amount of inference processing load can be reduced. can do.

Second Embodiment
Next, a second embodiment of the present invention will be described. In addition, about the part which becomes the structure similar to 1st Embodiment, the same code is attached and description is abbreviate | omitted.

  The second embodiment is different from the first embodiment in that an inference unit for calculating the certainty factor is provided on the server side. In the first embodiment, although the case where the inference unit for calculating the certainty factor is implemented in the in-vehicle apparatus has been described as an example, the inference unit may be implemented on the server side. When the inference unit is mounted on the server side, the communication unit of the server receives the predicted route output by the vehicle behavior prediction unit of the in-vehicle device by communication, and the inference unit on the server side calculates the certainty factor.

<Configuration of Vehicle Search System 210 According to Second Embodiment>
As shown in FIG. 16, the vehicle search system 210 according to the second embodiment of the present invention includes a plurality of in-vehicle devices 212 provided in a plurality of vehicles and vehicle information transmitted from the plurality of in-vehicle devices 212. And a server 214 for receiving and searching for vehicles satisfying the search condition. The plurality of in-vehicle devices 212 and the server 214 are connected via the network 16 such as the Internet.

[In-vehicle device 212]
The on-vehicle device 212 operates based on the position measurement unit 120, the vehicle sensor 124, the position information of the vehicle measured by the position measurement unit 120, and the speed of the vehicle detected by the vehicle sensor 124. And a computer 226 for predicting the

  The computer 226 includes a CPU, a RAM, and a ROM storing programs for executing processing routines described later, and is functionally configured as follows. The computer 226 is an information acquisition unit 128 for acquiring the position information (X, Y) of the current time t of the host vehicle measured by the position measurement unit 120 and the speed of the host vehicle detected by the vehicle sensor 124 A communication unit 230 that sequentially transmits vehicle information acquired by the acquisition unit 128 to the server 214 together with the vehicle ID, and transmits / receives data to / from the server 214, and position information of the own vehicle acquired by the information acquisition unit 128 And a vehicle behavior prediction unit 132 for predicting a predicted route of the own vehicle based on the speed of the own vehicle, and a task processing unit 136 for executing the task when the instruction information of the task transmitted from the server 214 is received. Have. The communication unit 230 is an example of a vehicle behavior information transmission unit and a vehicle information transmission unit.

  The communication unit 230 sequentially transmits the position information (X, Y) of the current time t of the vehicle acquired by the information acquisition unit 128 to the server 214. Further, the communication unit 230 receives the prediction result transmission request transmitted from the server 214. Further, when the communication unit 230 receives the prediction result transmission request transmitted from the server 214, the communication unit 230 transmits the predicted route predicted by the vehicle behavior prediction unit 132 to the server 214. The communication unit 230 also receives the instruction information transmitted from the server 214 and instructing the task to be performed by the vehicle.

[Server 214]
The server 214 is configured by a server including a CPU, a ROM storing programs for realizing processing routines to be described later, a RAM temporarily storing data, a memory as a storage unit, a network interface, etc. Functionally, it can be represented by a configuration including the reception unit 140, the communication unit 242, the data storage unit 144, the database 145, the simple prediction unit 146, the inference unit 234, the vehicle search unit 248, and the task generation unit 150. The communication unit 142 is an example of a prediction result transmission request transmission unit, and the simple prediction unit 146 is an example of a vehicle selection unit. The inference unit 234 is an example of search information generation means.

  The communication unit 242 receives vehicle position information (X, Y) transmitted from each of the plurality of on-vehicle devices 212. In addition, the communication unit 242 transmits a prediction result transmission request to each of the on-vehicle devices 212 of the vehicles selected as candidate vehicles by the simple prediction unit 146 described later. In addition, the communication unit 242 transmits instruction information generated by the task generation unit 150 described later to the on-vehicle device 212 of the vehicle.

  The inference unit 234, as information indicating whether or not the search condition included in the query is satisfied for each of the on-vehicle devices 212 based on each of the predicted routes of the vehicle received by the communication unit 242, the degree of the search condition Calculate the degree of certainty that represents

  The vehicle search unit 248 searches for a vehicle that satisfies the search condition based on the degree of certainty generated by the inference unit 234.

<Operation of vehicle search system>
Next, the operation of the vehicle search system 210 according to the second embodiment of the present invention will be described. When each of the vehicles equipped with the in-vehicle apparatus 212 travels and the server 214 is activated, the in-vehicle apparatus 212 of each vehicle executes a prediction process routine shown in FIG. 9 and the server 214 performs a search process routine shown in FIG. Run. Further, the inference processing routine shown in FIG. 14 and the certainty factor calculation processing routine shown in FIG. 15 are executed on the server 214 side.

  The other configuration and operation of the vehicle search system according to the second embodiment are the same as those of the first embodiment, and thus the description thereof is omitted.

  As described above, according to the vehicle search system of the second embodiment of the present invention, the in-vehicle device repeatedly predicts the predicted route of the vehicle until after a predetermined prediction time based on the vehicle information. When the server receives the prediction result transmission request transmitted from the server, the server transmits the predicted route of the predicted vehicle to the server, and the server receives the input search condition and the vehicle information transmitted from each of the plurality of in-vehicle devices. And select a candidate vehicle that satisfies the search condition from each of the plurality of vehicles, and transmit a prediction result transmission request to each of the in-vehicle devices of the vehicle selected as the candidate of the vehicle. The certainty factor is calculated for each of the in-vehicle devices based on the predicted route of the vehicle transmitted from each, and the vehicle search delay can be suppressed by searching for the vehicle based on the certainty factor.

  The present invention is not limited to the embodiment described with reference to the drawings, and various changes may be made without departing from the scope of the invention.

  For example, in the above embodiment, although the case of predicting the route of the vehicle has been described as an example, the present invention is not limited to this. For example, when the vehicle sensor 124 is configured using a car navigation system, a destination is input to the car navigation system, and the travel route of the vehicle is indicated to the driver, the driver has a high possibility of the car navigation system. Since it is considered that the route of (1) is used, improvements such as using the route as a predicted route are also conceivable.

  Further, in the above embodiment, the case of predicting the predicted route as the time series of the behavior of the vehicle has been described as an example, but the present invention is not limited thereto. For example, the destination is predicted as the time series of the behavior of the vehicle You may

  Further, in the above embodiment, the simple prediction unit has been described by way of example in which the certainty factor is calculated based on the position information of the vehicle. However, the present invention is not limited thereto. The degree of certainty can also be calculated based on angle information such as whether or not there is a road segment A (point A) ahead of the vector.

  In the above embodiment, the task generation unit 150 generates a task according to the certainty factor, and the task processing unit 136 executes the task. There is no need to create a task or execute a task.

Further, in the above-described embodiment, an example in which detecting the speed of the vehicle by the vehicle sensor 124, calculates the T (r _t) of the average required travel time r _t with the speed of the vehicle Although explained, it is not limited to this. For example, an average required travel time determined in advance for r _t may be used T (r _t).

  The storage medium for storing the program of the present invention is not particularly limited, and may be a hard disk or a ROM. Also, it may be a CD-ROM, a DVD disk, a magneto-optical disk or an IC card. Furthermore, the program may be downloaded from a server or the like connected to a network.

10, 210 Vehicle search system 12, 212 Vehicle-mounted device 14, 214 Server 16 Network 120 Position measurement unit 124 Vehicle sensor 126, 226 Computer 128 Information acquisition unit 130, 230 Communication unit 132 Vehicle behavior prediction unit 134, 234 Reasoning unit 136 Task processing Unit 140 Reception unit 142, 242 Communication unit 144 Data storage unit 145 Database 146 Simple prediction unit 148, 248 Vehicle search unit 150 Task generation unit

Claims (6)

A vehicle search system comprising: a plurality of in-vehicle devices provided in a plurality of vehicles; and an information processing device,
The in-vehicle device is
Vehicle information transmitting means for transmitting vehicle information on the vehicle to the information processing apparatus;
Vehicle behavior prediction means that repeatedly predicts a time series of the behavior of the vehicle based on the vehicle information until after a predetermined prediction time;
Whether or not the search condition is satisfied based on the time series of the behavior of the vehicle predicted by the vehicle behavior prediction means when the search condition regarding the position and time transmitted from the information processing apparatus is received Search information generation means for generating information representing the information;
Search information transmission means for transmitting information indicating whether the search condition is generated or not generated by the search information generation means to the information processing apparatus;
The information processing apparatus is
Vehicle selection means for selecting a candidate of the vehicle satisfying the search condition from each of the plurality of vehicles based on the input search condition and the vehicle information transmitted from each of the plurality of on-vehicle devices When,
Search condition transmitting means for transmitting the search condition to each of the on-vehicle devices of the vehicle selected as the vehicle candidate by the vehicle selection means;
A vehicle search system comprising: vehicle search means for searching for a vehicle satisfying the search condition based on information indicating whether the search condition is transmitted from each of the in-vehicle devices. A vehicle search system comprising: a plurality of in-vehicle devices provided in a plurality of vehicles; and an information processing device,
The in-vehicle device is
Vehicle information transmitting means for transmitting vehicle information on the vehicle to the information processing apparatus;
Vehicle behavior prediction means that repeatedly predicts a time series of the behavior of the vehicle based on the vehicle information until after a predetermined prediction time;
Vehicle behavior information transmitting means for transmitting the time series of the behavior of the vehicle predicted by the vehicle behavior predicting means to the information processing device when receiving the prediction result transmission request transmitted from the information processing device; , And
The information processing apparatus is
The candidate of the vehicle satisfying the search condition is selected from each of the plurality of vehicles based on the input search condition regarding position and time and the vehicle information transmitted from each of the plurality of in-vehicle devices Vehicle selection means,
A prediction result transmission request transmission unit that transmits the prediction result transmission request to each of the on-vehicle devices of the vehicle selected as the candidate of the vehicle by the vehicle selection unit;
Search information generation means for generating information indicating whether or not the search condition is satisfied for each of the in-vehicle devices based on the time series of the behavior of the vehicle transmitted from each of the in-vehicle devices;
A vehicle search system comprising: vehicle search means for searching for a vehicle satisfying the search condition based on the information indicating whether or not the search condition is generated which is generated by the search information generation means. The search condition is a search condition including a traveling position of a vehicle and a time interval,
The vehicle behavior prediction means repeatedly predicts, based on the vehicle information, the time series of the traveling position of the vehicle until a prediction time predetermined as the upper limit of the time interval included in the search condition.
The search information generation unit is configured to execute the travel position included in the search condition within the time section included in the search condition based on the time series of the travel position of the vehicle predicted by the vehicle behavior prediction unit. The vehicle search system according to claim 1, wherein information representing whether the vehicle travels is calculated.   The vehicle search system according to any one of claims 1 to 3, wherein the information indicating whether or not the search condition is satisfied is a degree of certainty indicating a degree of the search condition. For each of the retrieved vehicles,
The information processing apparatus is
Task generation means for generating instruction information for instructing a task to be executed by the vehicle, the task corresponding to the information indicating whether or not the search condition is satisfied;
Task transmission means for transmitting the instruction information generated by the task generation means to the in-vehicle device of the vehicle;
The in-vehicle device is
The vehicle search system according to any one of claims 1 to 4, further comprising task processing means for executing the task based on the instruction information transmitted from the information processing device. The task generation unit generates, for each of the searched vehicles, the instruction information instructing the presence or absence of the information presentation or the type of the presentation information according to the information indicating whether the search condition is satisfied. ,
The vehicle search system according to claim 5, wherein the task processing means executes a task of presenting information to an occupant of the vehicle based on the instruction information transmitted from the information processing apparatus.

Images