JP2005156350A - Destination estimating system, navigation system, and destination estimating method - Google Patents

Destination estimating system, navigation system, and destination estimating method Download PDF

Info

Publication number
JP2005156350A
JP2005156350A JP2003395387A JP2003395387A JP2005156350A JP 2005156350 A JP2005156350 A JP 2005156350A JP 2003395387 A JP2003395387 A JP 2003395387A JP 2003395387 A JP2003395387 A JP 2003395387A JP 2005156350 A JP2005156350 A JP 2005156350A
Authority
JP
Japan
Prior art keywords
destination
time
point
arrival
means
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
JP2003395387A
Other languages
Japanese (ja)
Inventor
Okihiko Nakayama
Fumio Seto
Masayuki Watabe
沖彦 中山
眞幸 渡部
史生 瀬戸
Original Assignee
Nissan Motor Co Ltd
日産自動車株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nissan Motor Co Ltd, 日産自動車株式会社 filed Critical Nissan Motor Co Ltd
Priority to JP2003395387A priority Critical patent/JP2005156350A/en
Publication of JP2005156350A publication Critical patent/JP2005156350A/en
Application status is Pending legal-status Critical

Links

Images

Abstract

<P>PROBLEM TO BE SOLVED: To estimate a destination, without being limited by the starting place and the departure time. <P>SOLUTION: For each time zone having a predetermined time width, and for each destination, the arrival frequency of the destination in the case of being on the move in the time zone is stored in a storage device 3. A destination estimating section 45 of a processing unit 4 estimates, that a location of which the arrival frequency stored in the storage device 3 is a predetermined value or larger in the time zone, corresponding to the current date and time, is set as the destination. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a destination prediction device that predicts a destination of a vehicle based on past travel data, a navigation device to which the destination prediction device is applied, and a method of predicting a destination.

  A device is known that records the past driving start point, driving end point, and the date and time at that time, and predicts the destination based on the recorded data, the date and time at the start of driving, and the driving start point. (See Patent Document 1).

JP-A-11-149596

  However, in the conventional apparatus, since the destination is predicted on the basis of the operation start date and time and the operation start position, the destination cannot be predicted if the date and position and the position at which the operation is started are different. was there.

(1) The destination prediction apparatus and the destination prediction method according to the present invention each store a movement history including at least the arrival point at the time of movement and the date and time of movement, and the measured current date and time. A destination is predicted based on the stored movement history.
(2) The navigation device according to the present invention calculates and displays a recommended route to the destination predicted by the destination prediction device.

  According to the destination prediction apparatus and the destination prediction method according to the present invention, since the destination is predicted based on the arrival point at the time of the past movement and the moving date and time included in the movement history, and the current date and time, The destination can be predicted regardless of the position at the start of movement.

  FIG. 1 is a diagram showing a configuration of an embodiment of a destination prediction apparatus according to the present invention. A destination prediction apparatus according to an embodiment is mounted on a vehicle and includes a timing device 1, a position detection device 2, a storage device 3, an arithmetic processing device 4, and an output device 5. The timing device 1 is a device that measures the current date and time, and is, for example, a clock with a calendar function. The position detection device 2 includes a vehicle speed sensor and a gyro sensor, for example, and detects the current position of the vehicle by autonomous navigation.

  The storage device 3 stores map data including shape data such as roads and backgrounds for map display, character data such as place names, road network data for route calculation, and the like, and a storage management unit of the arithmetic processing device 4 described later. The past travel history data created by 43 and the travel frequency of each road are stored. As the storage device 3, for example, an HDD capable of writing and updating stored contents can be used.

  The arithmetic processing unit 4 includes a CPU and an internal memory (not shown), and has a map data search unit 41, a map image creation unit 42, a storage management unit 43, a traveling direction detection unit 44, A ground prediction unit 45, a familiarity detection unit 46, and a guidance information providing unit 47 are provided. The map data search unit 41 reads map data around the current position from the map data stored in the storage device 3 based on the current position of the vehicle detected by the position detection device 2.

  The map image creation unit 42 creates map image data to be output to the output device 5 described later, based on the map data read by the map data search unit 41. The storage management unit 43 creates vehicle travel history data based on the date and time measured by the timing device 1 and the vehicle position detected by the position detection device 2, and stores it in the storage device 3. A method for creating the travel history data will be described later. The traveling direction detection unit 44 detects the approximate direction in which the vehicle travels based on the change in the vehicle position detected by the position detection device 2.

  The destination predicting unit 45 is based on the date and time detected by the timing device 1, the approximate traveling direction detected by the traveling direction detecting unit 44, and past travel history data stored in the storage device 3. Predict your destination. A destination prediction method will be described later. The familiarity detection unit 46 detects the familiarity of the driver with respect to each road based on past travel history data stored in the storage device 3.

  The guidance information providing unit 47 creates guidance information to be presented to the driver based on the destination predicted by the destination prediction unit 45 and the familiarity of each road detected by the familiarity detection unit 46. In addition, since the destination prediction apparatus in one embodiment functions also as a car navigation apparatus, the recommended route to the predicted destination is also calculated. The calculation of the recommended route can be performed by the guidance information providing unit 47, for example.

  The output device 5 is a display for display such as a liquid crystal monitor or a CRT, for example, and displays the map image data created by the map image creating unit 42 and the guidance information created by the guidance information providing unit 47. Further, when providing guidance information created by the route guidance or the guidance information providing unit 47 by voice, a speaker is also included in the output device 5.

  FIG. 2 is a diagram for explaining the principle of the destination prediction method, and shows past travel history data. In FIG. 2, for the sake of simplicity, traveling history data for one week from July 13th to 19th is shown.

  Note that the labels “A” to “E” in FIG. 2 are attached for convenience in order to identify the departure place and the arrival place. For example, A is a home, B is a restaurant, C is a work place, D Is a bookstore and E is a friend's house. When the travel history data as shown in FIG. 2 is stored in the storage device 3, the information given as the departure point / arrival point is, for example, only the latitude / longitude information, and what is the specific location of the point? There is no need to identify. Therefore, in the destination prediction apparatus in one embodiment, the departure point and the arrival point are identified by labels such as “A” to “E”.

  According to the running history shown in FIG. 2, on July 13th, the point labeled “A” departed about 18:30 and arrived at “B” approximately 30 minutes later and arrived at “B”. About 2 hours later, it returns to “A” again. In addition, from Monday to Friday (14th to 18th), depart “A” around 8 o'clock in the morning, arrive at “C” for about an hour and a half, and leave “C” around 20 o'clock. And return to "A". However, on Friday, we stopped by “D” on the way back to “A”. On Saturday, I left “A” in the morning, went to “E”, spent the evening, and then returned to “A” again.

  In the destination prediction apparatus in one embodiment, the destination is predicted based on the day of the week and the time when the vehicle travels, and past travel history data. However, there may be no travel history in the past in the time corresponding to the day of the week and the time when the vehicle travels. For example, suppose that a driver was moving in a vehicle at 20:30 on a certain Thursday. From the past driving history shown in FIG. 2, when a portion corresponding to the date and time condition of “20:30 on Thursday” is examined, the vehicle is running on 20:30 on Thursday (July 17). There wasn't. Therefore, it is impossible to simply predict the destination in light of the past travel history.

  However, when this date and time condition is interpreted as “20:30 on weekdays” in an expanded manner and the history corresponding to this date and time is examined (see the dotted line in FIG. 2), the corresponding data exists. That is, it can be seen that this user was traveling around 20:30 on the 14th, 15th, 16th and 18th. Among these, the arrival point is A for three days from Monday to Wednesday. Therefore, it can be predicted that “if it is traveling at 20:30 on weekdays, it will head toward A with a probability of 3/4, that is, 75% based on the past traveling history”. The destination prediction apparatus in one embodiment predicts a destination based on such a principle.

  FIG. 3 is a diagram illustrating an example of vehicle travel data stored in the storage device 3. When data in the time chart format shown in FIG. 2 is stored, there is a possibility that the capacity of the storage device 3 is insufficient because the increase in the amount of data is large. Therefore, the destination prediction apparatus according to the embodiment stores the travel history data in the form as shown in FIG.

  In the travel history data shown in FIG. 3, one day is divided into 10-minute time segments, and for each time segment of each day of the week, the arrival point and arrival frequency when traveling within that time segment are stored. . Therefore, for example, as shown in FIG. 3, when a date condition of 20:20 to 20:30 on Thursday is given, an arrival point and an arrival frequency when traveling in the past that match the condition are detected. Can do. In the example shown in FIG. 3, it arrives at the A point seven times, the B point twice, and the C point once.

  A method of storing the frequency for each arrival point for each time segment will be described with reference to FIG. FIG. 4A shows data stored when no travel history data is recorded and the vehicle departs from point C at 20:03 and arrives at point A at 20:46. In this case, as shown in FIG. 4A, the arrival frequency 1 of A is written in five time segments ranging from 20:00 to 20:50. In FIGS. 4A to 4F, the newly written portion is underlined.

  Next, if it departs from point C at 19:44 on the same day and arrives at point A at 20:29, as shown in FIG. 4 (b), 19:40 to 20:00 The arrival frequency 1 of A is written in the two time segments, and 1 is added to the arrival frequency of A and becomes 2 in the three time segments of 20:00 to 30 minutes.

  After that, the departure from point C at 20:17 on the same day and the arrival at point A at 21:01 are shown in Fig. 4 (c), and point C is departed at 20:32 on the same day. FIG. 4 (d) shows the result when arriving at point A at 21:15. Here, when the traveling history of departure from point C at 19:43 on the same day and arrival at point B at 20:16 is added, the arrival frequency of label B is updated as shown in FIG. 4 (e). Added to. Further, FIG. 4 (f) shows a result when a travel history is added that departs from point B at 20:42 and arrives at point A at 21:18 on the same day.

  FIG. 5 is a flowchart showing a process performed by the storage management unit 43 of the arithmetic processing unit 4, that is, a procedure for creating travel history data of the vehicle. In step S10, the time when the system is started, that is, the time immediately after the vehicle is started, is stored as the travel start time in the time segment format as shown in FIG. For example, if it is activated at 7:59 on Monday, the travel start time section T1 is set from 7:50 to 8:00 on Monday. When the travel start time section T1 is stored, the process proceeds to step S20.

  In step S20, it is determined whether or not there has been an instruction to stop the system. If it is determined that the vehicle is running and there is no instruction to stop the system, the process stands by in step S20. When the system is stopped, for example, even when the engine is stopped, power is supplied to the arithmetic processing unit 4 using a backup power source (not shown) so that the processing after step S30 can be performed.

  In step S30, the time when the system stop instruction is issued is stored as the travel end time section T2, and the process proceeds to step S40. In step S40, the position (x, y) when the vehicle is stopped is acquired from the position detection device 2, and the process proceeds to step S50. In step S50 to step S70, a process of attaching a label to the position (x, y) at the time of vehicle stop acquired in step S40 is performed.

  In step S50, based on the traveling history data stored in the storage device 3, is there a point labeled within the predetermined distance S from the position (x, y) at the time of vehicle stop acquired in step S40. Determine whether or not. If it is determined that there is a labeled point, the process proceeds to step S60, and if it is determined that there is no point, the process proceeds to step S70.

  In step S60, the label of the point P with the label existing within the predetermined distance S from the position (x, y) when the vehicle is stopped is set as the label of the point of arrival at this time. However, when there are a plurality of points labeled within the predetermined distance S, the label of the point P closest to the position (x, y) when the vehicle is stopped is set as the label of the point of arrival this time. In this case, the average position of the already registered position of the point P and the current stop position (x, y) is re-registered as the defined position of the point P.

  On the other hand, in step S70, since there is no labeled point within the predetermined distance S from the position (x, y) when the vehicle is stopped, a new label is attached to the point (x, y) that has arrived this time. And register. If the arrival point label is confirmed in step S60 or step S70, the process proceeds to step S80.

  In step S80, in the travel history data stored in the storage device 3, the time series T1 at the start of travel obtained in step S10 to the time segment T2 at the end of travel obtained in step S30 are continuous in time series. Increase the arrival frequency of point P by 1 for all time segments. Details of the processing performed in step S80 have been described with reference to FIGS. 4A to 4F, and detailed description thereof will be omitted. When the process of step S80 is performed, the process performed by the storage management unit 43 ends.

  FIG. 6 is a flowchart showing the processing contents for predicting the destination. The process starting from step S100 is performed at regular intervals. In step S100, the traveling direction detection unit 44 detects the approximate traveling direction of the vehicle. For example, by calculating the difference between the current position detected by the position detection device 2 and the position detected 5 minutes ago, the direction advanced in 5 minutes is obtained. When the approximate traveling direction of the vehicle is detected, the process proceeds to step S110.

  In step S <b> 110, the destination prediction unit 45 determines the date and time conditions for the travel history search based on the current time measured by the timing device 1. For example, if the current time is 20:27 on Thursday, the time history shown in FIG. 3 includes the current time, that is, 20:20 to 30 on Thursday. And When the date and time conditions for the travel history search are determined, the process proceeds to step S120.

  In step S120, it is determined whether or not there is an arrival point whose frequency is equal to or greater than a predetermined value in the travel history corresponding to the date and time condition defined in step S110. For example, when a point having a frequency of 5 or more is detected in the travel history shown in FIG. 3, A having a frequency of 7 is detected. However, when there are a plurality of arrival points whose frequency is equal to or greater than a predetermined value, the plurality of arrival points are detected. The reason for detecting an arrival point with a frequency equal to or greater than a predetermined value is to avoid a point that has been visited only once or twice as a destination. If the determination in step S120 is affirmed, the process proceeds to step S130, and if not, the process proceeds to step S160.

  In step S130, it is determined whether or not the arrival point detected in step S120 is present in the traveling direction of the vehicle determined in step S100. However, here, it is not required that the direction of the arrival point based on the current position and the traveling direction of the vehicle completely coincide with each other. For example, the arrival point is within a predetermined direction range centering on the traveling direction of the vehicle. If there is, the determination in step S130 is affirmed. If the determination in step S130 is affirmed, the process proceeds to step S140, and if not, the process proceeds to step S160.

  In step S140, the arrival point with the highest frequency that exists in the traveling direction of the vehicle is set as the predicted destination. When the predicted destination is set, the process proceeds to step S150. On the other hand, in step S160, since there is no data that is the target of the predicted destination, it is determined whether or not the date and time conditions defined in step S110 can be expanded. The expansion of the date / time condition is to set a time segment larger than the time segment including the current time set in step S110 in order to make the condition for selecting the arrival point loose.

  There are two methods for expanding the date and time condition: a method of extending the date and a method of extending the time zone. The expansion of the date is to include a plurality of time segments in the date and time conditions in units of days. For example, the condition of “Thursday” is expanded to “weekdays”. Therefore, the search condition of “20:20 to 30 on Thursday” becomes “20:20 to 30 on Monday to Friday” due to the expansion of the date. Further, when the current time is Saturday, it can be extended to “weekend” including Saturday and Sunday.

  On the other hand, the expansion of the time zone is to consider a plurality of unit time segments shown in FIG. 3 as one condition. For example, with respect to the time division of “20: 20-30 minutes”, the unit time divisions one by one before and after are collectively referred to as “20: 10-40 minutes”. Moreover, it is good also as "20: 00-30 minutes" by making time for every 30 minutes into one unit division.

  If it is determined in step S160 that the date and time conditions can be expanded, the process proceeds to step S180. On the other hand, for example, when extending the time zone when the upper limit of the extension is set to 1 hour, the time zone is extended several times, so if the time zone is extended again, the upper limit of 1 hour is exceeded. If not, it is determined that the date / time condition cannot be expanded, and the process proceeds to step S170. In step S170, it is determined that the destination cannot be predicted, and the destination setting process ends.

  In step S180, the extended date / time condition is set, and the process returns to step S120, and the process for predicting the destination is performed again within the range of the extended date / time condition. When returning from step S180 to step S120, the frequency counting method for each point in the travel history must be changed depending on whether the date is extended or the time zone is extended. When the date is extended, the traveling history of each day of the week is considered to be independent, and the arrival frequency at the same point on each day can be simply added. For example, if Thursday is extended on weekdays, the journey toward Point A on Thursday and the journey toward Point A during the same time zone on Friday are separate journeys and run continuously for more than 24 hours. It is hard to think that it was.

  On the other hand, in the method of extending the time zone, the journey toward the point A from 20:10 to 20 on Thursday and the journey toward the point A from 20:20 to 30 are the same. Since the possibility is high, it is not possible to simply add both frequencies. That is, when the arrival frequency of the former A is 6 and the arrival frequency of the latter A is 7, the arrival frequency of A in the expanded time zone cannot be 13. Therefore, in the case of time zone expansion, the maximum value among the arrival frequencies at the same point recorded in each unit time segment is set as the frequency at the arrival point. This point will be described with reference to FIG.

  FIG. 7 is a diagram illustrating an example of a travel history from 20:00 to 20:50. If the maximum value among the arrival frequencies at each point is detected when the condition of 20: 20-30 is extended to 20: 10-40, A is 7, B is 3, C is 1, D is 1 Therefore, these values are used as the frequency of each arrival point in the expanded time zone.

  In step S150, the guidance information providing unit 47 creates guidance information up to the predicted destination set in step S140. There are various types of guidance information, but because the destination is predicted based on past driving history, the user has visited the predicted destination many times. it is conceivable that. Therefore, if the route to the predicted destination is calculated and information is provided so that left / right turn information is presented each time the intersection is approached, it is cumbersome and inconvenient for the user. There is a high possibility that

  Therefore, the destination prediction apparatus in one embodiment calculates a plurality of routes to the predicted destination, and provides information that highlights current traffic jam information on the calculated route. The traffic jam information can be acquired by using a system such as VICS, for example.

  FIG. 8 is a diagram illustrating an example of route guidance displayed on the display device that is the output device 5. On the left side of the screen, as with a conventional car navigation system, a map centered on the current vehicle position is displayed in a scale and composition (planar display, bird's-eye view display, etc.) set by the user, and on the right side of the screen , Displaying multiple routes to the expected destination. Note that a flag indicating the arrival point is set at the predicted destination.

  On the right side of the screen shown in FIG. 8, the map is displayed in a wide-scale that includes the entire route from the current position to the predicted destination. However, if the user is familiar with the route, the route outline can be identified. Conceivable. Therefore, by highlighting traffic information on the route to the expected destination, the user can encounter traffic congestion on which route through which route, or traffic congestion on which route can be avoided. I can understand. Although a method of displaying all traffic information on a map as in a conventional navigation device is conceivable, there is a possibility that what is being displayed cannot be identified on a wide-scale map. Therefore, the destination prediction apparatus according to the embodiment displays only the traffic jam information along the predicted traveling direction on a plurality of routes to the predicted destination.

  Here, the reason why the screen is divided into two parts will be described. Because the destination prediction is not 100% accurate, the user is not always heading for the predicted destination. Therefore, when the route to the predicted destination and the traffic jam information are displayed on one screen, if the prediction of the destination is lost, the display of the route to the predicted destination becomes an obstacle to the driver.

  In addition, when the destination is predicted, a method of inquiring the user about the suitability of the predicted destination can be considered, but this requires the user to input an answer. Therefore, in the destination prediction apparatus in one embodiment, the screen of the display device is divided into two, and the normal map display, that is, the map around the vehicle position is displayed on the left side of the screen, and the right side of the screen Then, the whole route to the predicted destination was simply displayed. As a result, even when the destination is unpredictable, the minimum necessary peripheral geographic information can be continuously provided without requiring the user to operate the system or troublesome instructions.

  Therefore, the user is not forced to operate the system even if the destination is unpredicted, and at any time when the route display to the predicted destination seems to be in the way, An operation to return to the display of one screen can be performed. In addition, as described above, the destination prediction process is repeatedly performed at regular time intervals. Therefore, even if the predicted result is deviated at a certain time, another point is predicted as the destination with the passage of time and the movement of the vehicle. Or a prediction result that the corresponding point does not exist is obtained. Therefore, unnecessary predicted destination information is not displayed even if the user does not operate the system.

-Modification-
Another example of performing route guidance to the predicted destination will be described with reference to FIG. FIG. 9 is a diagram illustrating another example of a display screen for performing route guidance to a predicted destination. Here, route guidance based on the familiarity of each road detected by the familiarity detecting unit 46, that is, the past traveling frequency is performed.

  Since the predicted destination calculated by the destination prediction apparatus in one embodiment is a place where the user has visited many times in the past, it is obvious that the familiarity of the surrounding geography is high. However, the current position is not always highly familiar. For example, if you are a user who goes home on Sunday night, if you are driving on Sunday night, your home is set as the predicted destination even if you are driving for the first time . In such a case, if a route from a current position with a low degree of familiarity to a predicted destination with a high degree of familiarity is calculated, the road always joins a road with a high degree of familiarity at any position on the calculation route.

  Therefore, in another example of performing route guidance to the predicted destination shown in FIG. 9, based on the familiarity of each road detected by the familiarity detecting unit 46, a highly familiar road on the route to the predicted destination. A junction point is detected, and detailed guidance such as turning left and right is performed until the detected junction point.

  The storage management unit 43 records the passing frequency for each road in the storage device 3 every time it passes through each road. The familiarity detection unit 46 reads out the passing frequency of the target road from the storage device 3 and outputs a higher familiarity as the passing frequency increases. Therefore, a point where the degree of familiarity exceeds a predetermined threshold on the route from the current position to the predicted destination can be detected as a merging point from a road with a low degree of familiarity to a road with a high degree of familiarity.

  On the right side of the screen shown in FIG. 9, as with the right side screen shown in FIG. 8, a plurality of routes to the predicted destination are shown, and further, the junction point calculated by the above-described method is clearly shown. The name given to the confluence is the name of the intersection when the intersection name is attached, and if the name is not attached, the name of the place closest to the point is used. Is displayed. As these intersection names and place names, those stored in the storage device 3 as map display data can be used.

  On the left side of the screen shown in FIG. 9, as with the left side of the screen of FIG. 8, a map with the current position of the vehicle is displayed and the direction of the merging point to the high familiarity road is displayed. In other words, the route to the calculated predicted destination is shown superimposed on the normal current position map display, and the name of the high-skilled road junction that is encountered at the end of the route at the end of the screen. Is superimposed and displayed. By displaying in this way, even if the user is not familiar with the geography around the current position, the route to the well-known confluence is shown in more detail, and the route guidance is accurately provided within a range that does not bother the user. Can do.

  The information display as shown in FIG. 9 is considered to be effective in a situation where, for example, a long-distance return road can be returned if it is possible to get on at high speed for the time being. In the example shown in FIG. 9, since different confluences are obtained in a plurality of routes, information on which route is well known and which place is well known can be provided.

  As described above, according to the destination prediction device in one embodiment, the movement history including at least the arrival point at the time of movement and the date and time of movement is stored in the storage device 3, and the current date and time are stored in the storage device 3. The destination is predicted based on the movement history that has been recorded. Thereby, the destination can be predicted without being limited to the place and time at the start of movement. That is, the destination can be predicted at an arbitrary time point during movement.

  In particular, in a time zone corresponding to the current day of the week and time, since a point where the arrival frequency when moving in the past is a predetermined value or more is predicted as a destination, highly accurate destination prediction can be performed. Further, it is possible to predict a destination with higher accuracy by predicting, as a destination, a point that exists in the traveling direction of the vehicle among points having an arrival frequency of a predetermined value or more.

  In the destination prediction apparatus according to the embodiment, when the destination cannot be predicted, a point where the arrival frequency when moving in the past is a predetermined value or more in a time zone in which the time zone corresponding to the current date and time is expanded Therefore, it is possible to predict an appropriate destination without being constrained by uniform destination prediction processing.

  According to the destination prediction apparatus in one embodiment, since the destination prediction process is repeatedly performed until the arrival point is reached, unlike the conventional destination prediction apparatus, only the information at the time of departure is limited. Flexible destination prediction that is not possible. For example, if you are going to work earlier than usual, even if you cannot predict your work location as the destination at the time of departure, if the destination prediction process is performed when you approach the normal departure time, The predicted destination can be set.

  Also, even if you stop by somewhere on the way to work, even if you can't predict where you are going, you can set your work as a predicted destination when you go from work to work. be able to. In other words, the user can concentrate on driving because the destination prediction process is repeated until an appropriate destination is predicted, even if the user does not give any specific input to the system. Route information to the predicted destination can be obtained automatically.

  As described above, the destination prediction device in one embodiment also has a function as a navigation device. In this navigation device, the map around the current vehicle position is displayed on one of the two screens, and the traffic information on the recommended route to the predicted destination is displayed on the other screen. It is possible to provide useful information to the user without impairing the convenience of the user.

  In addition, the travel history stored in the storage device 3 includes information on the frequency of passage of roads that have passed in the past, and among the roads included in the recommended route to the predicted destination, the road having a frequency of passage of a predetermined value or more. By emphasizing and displaying information for reaching the user, the user can obtain route guidance information to a well-known road.

  The present invention is not limited to the embodiment described above. For example, the position detection device 2 detects the current position of the vehicle by autonomous navigation, but may detect the position of the host vehicle by satellite navigation using a GPS sensor. The traveling direction detection unit 44 detects the approximate direction in which the vehicle travels based on the change in the vehicle position, but other methods may be used. For example, an arithmetic average of the traveling direction detected in the current process and the traveling direction detected in the previous process can be obtained to obtain the current approximate traveling direction.

  In the route guidance screen shown in FIG. 8, the congestion information on the recommended route to the predicted destination is superimposed and displayed. However, the traffic information that is not on the recommended route can also be displayed at the same time. However, when a plurality of pieces of traffic information are displayed, the visibility may be lowered, so it is necessary to limit the traffic information to be displayed. For example, on a large road of a predetermined size or larger, traffic congestion information at a point that roughly matches the direction from the current position to the predicted destination and that is more than a predetermined distance from any of the calculated multiple routes Is displayed.

  In step S130 of the flowchart shown in FIG. 6, it is determined whether or not an arrival point with an arrival frequency equal to or higher than a predetermined value exists in the traveling direction of the vehicle. You may make it determine whether it exists. For example, the distance L1 between the current position of the vehicle at a certain time T and a certain point is compared with the distance L2 between the current position of the vehicle and the certain point at time T + Δt after the lapse of time Δt, and if L2 <L1, It can be determined that the vehicle is approaching a certain point. In this case, the vehicle traveling direction detection unit 44 may calculate the distance between the host vehicle and an arrival point whose arrival frequency is a predetermined value or more instead of detecting the traveling direction of the vehicle.

  Further, the present invention can also be applied to a system for exchanging various information between a vehicle and an information center. In other words, the travel data of the vehicle can be transmitted to the information center via radio, and after the destination is predicted based on the accumulated travel data, the predicted destination can be transmitted to the vehicle. .

  The correspondence between the constituent elements of the claims and the constituent elements of the embodiment is as follows. That is, the timing device 1 is a timing unit, the storage unit 3 is a storage unit, the destination prediction unit 45 of the arithmetic processing unit 4 is a prediction unit, the traveling direction detection unit 44 is a vehicle state determination unit and a traveling direction detection unit, The guidance information providing unit 47 constitutes recommended route calculation means, and the output device 5 constitutes display means. In addition, unless the characteristic function of this invention is impaired, each component is not limited to the said structure.

The figure which shows the structure of one Embodiment of the destination prediction apparatus by this invention. The figure which shows past driving history data in order to explain the principle of the destination prediction method The figure which shows an example of the driving data of the vehicle memorize | stored in a memory | storage device 4A to 4F are diagrams for explaining a method of storing the frequency for each arrival point for each time segment. Flow chart showing the procedure for creating vehicle travel history data Flowchart showing processing contents for predicting destination The figure which shows an example of the driving | running | working log | history from 20:00 to 20:50 The figure which shows an example of the route guidance displayed on a display apparatus The figure which shows another example of the route guidance displayed on a display apparatus

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Time measuring device 2 ... Position detection device 3 ... Storage device 4 ... Arithmetic processing device 5 ... Output device 41 ... Map data search part 42 ... Map image creation part 43 ... Storage management part 44 ... Travel direction detection part 45 ... Destination prediction Unit 46 ... Familiarity detection unit 47 ... Guide information providing unit

Claims (13)

  1. A time measuring means for measuring the date and time;
    Position detecting means for detecting the current position of the vehicle;
    Storage means for storing a movement history including at least the arrival point detected by the position detection means, and the moving date and time measured by the time measuring means;
    A destination prediction apparatus comprising: a prediction unit that predicts a destination based on a current date and time measured by the timing unit and a movement history stored in the storage unit.
  2. The destination prediction apparatus according to claim 1,
    The travel history stored in the storage means is a destination in which date / time data from the time of the start of travel to the time of arrival is associated with information on the arrival point at that time. Prediction device.
  3. In the destination prediction apparatus according to claim 1 or 2,
    The storage means stores, for each arrival point, the arrival frequency of the arrival point when moving to the time zone for each time zone having a predetermined time width,
    The prediction means predicts a point where the arrival frequency stored in the storage means is a predetermined value or more as a destination in a time zone corresponding to the current date and time measured by the time measuring means. Land prediction device.
  4. In the destination prediction apparatus according to claim 3,
    The storage means stores an arrival frequency for each arrival point for each time zone of each day of the week,
    The destination prediction apparatus, wherein the prediction means predicts a point where the arrival frequency is a predetermined value or more as a destination in a time zone corresponding to the current day of the week and time measured by the time measuring means.
  5. The destination prediction apparatus according to claim 3 or 4,
    Vehicle status determination means for determining a relationship between a point where the arrival frequency is equal to or higher than a predetermined value and the position of the vehicle;
    The destination prediction apparatus, wherein the prediction means predicts, as a destination, a point at which the vehicle situation determination unit determines that the vehicle is approaching among points where the arrival frequency is equal to or higher than a predetermined value.
  6. The destination prediction apparatus according to claim 5,
    The vehicle status determination means calculates a distance between a point where the arrival frequency is equal to or higher than a predetermined value and the position of the vehicle,
    The predicting unit determines that the distance between the point where the arrival frequency calculated by the vehicle condition determining unit is equal to or greater than a predetermined value and the vehicle among the points where the arrival frequency is equal to or greater than a predetermined value is decreasing. A destination prediction apparatus for predicting a point as a destination.
  7. The destination prediction apparatus according to claim 5,
    The vehicle state determination means includes a traveling direction detection means for detecting the traveling direction of the vehicle,
    The destination prediction apparatus, wherein the prediction means predicts a point existing in a traveling direction detected by the traveling direction detection means among destinations having an arrival frequency equal to or greater than the predetermined value as a destination.
  8. In the destination prediction apparatus in any one of Claims 3-7,
    In the case where the predicting unit could not predict the destination, the arrival frequency stored in the storage unit in the time zone expanded from the time zone corresponding to the current date and time measured by the time measuring unit is A destination prediction apparatus that predicts a point that is a predetermined value or more as a destination.
  9. In the destination prediction apparatus in any one of Claims 1-8,
    The destination prediction apparatus, wherein the prediction means repeatedly performs a destination prediction process until the arrival point is reached.
  10. The destination prediction apparatus according to any one of claims 1 to 9,
    Recommended route calculation means for calculating a recommended route to the destination predicted by the prediction means;
    A navigation device comprising display means for displaying a recommended route calculated by the recommended route calculation means.
  11. The navigation device according to claim 10,
    The display means displays a map around the current vehicle position on one screen obtained by dividing the screen into two, and displays traffic information on a recommended route to the predicted destination on the other screen. Navigation device.
  12. The navigation device according to claim 10 or 11,
    The movement history stored by the storage means includes information on the frequency of passage of the road that has passed until the arrival point is reached,
    The display means highlights and displays information for arriving at a road having a predetermined frequency or more among roads included in the recommended route calculated by the recommended route calculation means. apparatus.
  13. Every time we move, we will store a movement history that includes at least the arrival point at the time of movement and the date and time of movement,
    Measure the current date and time
    A destination prediction method, wherein a destination is predicted based on the measured current date and time and the stored movement history.
JP2003395387A 2003-11-26 2003-11-26 Destination estimating system, navigation system, and destination estimating method Pending JP2005156350A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2003395387A JP2005156350A (en) 2003-11-26 2003-11-26 Destination estimating system, navigation system, and destination estimating method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
JP2003395387A JP2005156350A (en) 2003-11-26 2003-11-26 Destination estimating system, navigation system, and destination estimating method

Publications (1)

Publication Number Publication Date
JP2005156350A true JP2005156350A (en) 2005-06-16

Family

ID=34721176

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2003395387A Pending JP2005156350A (en) 2003-11-26 2003-11-26 Destination estimating system, navigation system, and destination estimating method

Country Status (1)

Country Link
JP (1) JP2005156350A (en)

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007085938A (en) * 2005-09-22 2007-04-05 Alpine Electronics Inc Navigation system and map display method
WO2008136193A1 (en) 2007-05-02 2008-11-13 Panasonic Corporation Destination estimating device, destination estimating method and navigation device
JP2009186252A (en) * 2008-02-05 2009-08-20 Toyota Motor Corp On-vehicle navigation device
JP2010048671A (en) * 2008-08-21 2010-03-04 Aisin Aw Co Ltd Navigation system
JP2010160755A (en) * 2009-01-09 2010-07-22 Aisin Aw Co Ltd Device and method for predicting travel pattern
US8068977B2 (en) 2006-09-28 2011-11-29 Panasonic Corporation Destination prediction apparatus and method thereof
WO2012056526A1 (en) * 2010-10-27 2012-05-03 トヨタ自動車株式会社 Navigation device
JP2012098307A (en) * 2012-02-07 2012-05-24 Pioneer Electronic Corp Navigation device, control method, program, and memory medium
WO2015120746A1 (en) * 2014-02-12 2015-08-20 华为技术有限公司 Method and device for predicting destination of user
JP2015152575A (en) * 2014-02-19 2015-08-24 三菱電機株式会社 Traffic jam information image creation device, traffic jam information image display device, and traffic jam information image creation method
JP2016184311A (en) * 2015-03-26 2016-10-20 日産自動車株式会社 Shared vehicle management apparatus and shared vehicle management method
JP2018155627A (en) * 2017-03-17 2018-10-04 本田技研工業株式会社 Navigation device, navigation method, and navigation program

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4731263B2 (en) * 2005-09-22 2011-07-20 アルパイン株式会社 Navigation device and map display method
JP2007085938A (en) * 2005-09-22 2007-04-05 Alpine Electronics Inc Navigation system and map display method
US8068977B2 (en) 2006-09-28 2011-11-29 Panasonic Corporation Destination prediction apparatus and method thereof
WO2008136193A1 (en) 2007-05-02 2008-11-13 Panasonic Corporation Destination estimating device, destination estimating method and navigation device
US8073615B2 (en) 2007-05-02 2011-12-06 Panasonic Corporation Destination-prediction apparatus, destination-prediction method, and navigation apparatus
JP2009186252A (en) * 2008-02-05 2009-08-20 Toyota Motor Corp On-vehicle navigation device
JP2010048671A (en) * 2008-08-21 2010-03-04 Aisin Aw Co Ltd Navigation system
JP2010160755A (en) * 2009-01-09 2010-07-22 Aisin Aw Co Ltd Device and method for predicting travel pattern
WO2012056526A1 (en) * 2010-10-27 2012-05-03 トヨタ自動車株式会社 Navigation device
JP2012098307A (en) * 2012-02-07 2012-05-24 Pioneer Electronic Corp Navigation device, control method, program, and memory medium
WO2015120746A1 (en) * 2014-02-12 2015-08-20 华为技术有限公司 Method and device for predicting destination of user
JP2015152575A (en) * 2014-02-19 2015-08-24 三菱電機株式会社 Traffic jam information image creation device, traffic jam information image display device, and traffic jam information image creation method
JP2016184311A (en) * 2015-03-26 2016-10-20 日産自動車株式会社 Shared vehicle management apparatus and shared vehicle management method
JP2018155627A (en) * 2017-03-17 2018-10-04 本田技研工業株式会社 Navigation device, navigation method, and navigation program

Similar Documents

Publication Publication Date Title
US9228850B2 (en) System and method for presenting a computed route
EP2771872B1 (en) Methods and systems for determining information relating to the operation of traffic control signals
KR20150143822A (en) Methods and apparatus for providing travel information
EP1111340B1 (en) Method and system of route selection
EP1521058B1 (en) Guiding device, system and method
US7739029B2 (en) Navigation apparatus and method with traffic ranking and display
JP4548460B2 (en) Navigation device
DE102008024777B4 (en) Method and device for estimating traffic information and motor vehicle navigation device
DE10356695B4 (en) Navigation system
JP5024134B2 (en) Travel information creation device, travel information creation method and program
US7630828B2 (en) Destination prediction device and destination prediction method
US7590483B2 (en) Traffic information management system
EP0901001B1 (en) Method and apparatus for displaying current position of a vehicle
US6529822B1 (en) Navigation system with zoomed maneuver instruction
US7657370B2 (en) Navigation apparatus, navigation system, and navigation search method
KR100348953B1 (en) Route searching device
US7233861B2 (en) Prediction of vehicle operator destinations
US7623963B2 (en) In-vehicle navigation device
DE102007037920B4 (en) Traffic condition prediction device
JP4511426B2 (en) Vehicle navigation device
KR101297909B1 (en) Path searching method, path guiding system, navigation device and statistics processing server
US9261376B2 (en) Route computation based on route-oriented vehicle trajectories
US6961658B2 (en) Method, system and article of manufacture for identifying regularly traveled routes
JP3960851B2 (en) Navigation device
EP1271103B1 (en) Navigation system, server system for a navigation system, and computer-readable information recorded medium in which destination prediction program is recorded

Legal Events

Date Code Title Description
A621 Written request for application examination

Effective date: 20060925

Free format text: JAPANESE INTERMEDIATE CODE: A621

RD02 Notification of acceptance of power of attorney

Free format text: JAPANESE INTERMEDIATE CODE: A7422

Effective date: 20080624

RD02 Notification of acceptance of power of attorney

Free format text: JAPANESE INTERMEDIATE CODE: A7422

Effective date: 20080605

RD04 Notification of resignation of power of attorney

Effective date: 20081014

Free format text: JAPANESE INTERMEDIATE CODE: A7424

A977 Report on retrieval

Effective date: 20090127

Free format text: JAPANESE INTERMEDIATE CODE: A971007

A131 Notification of reasons for refusal

Effective date: 20090224

Free format text: JAPANESE INTERMEDIATE CODE: A131

A521 Written amendment

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20090422

A02 Decision of refusal

Free format text: JAPANESE INTERMEDIATE CODE: A02

Effective date: 20091020