WO2011046185A1

WO2011046185A1 - Vehicle-mounted device, travel characteristic data generation device, and vehicle-mounted information system

Info

Publication number: WO2011046185A1
Application number: PCT/JP2010/068083
Authority: WO
Inventors: 憲一郎山根; 淳輔藤原
Original assignee: クラリオン株式会社
Priority date: 2009-10-14
Filing date: 2010-10-14
Publication date: 2011-04-21
Also published as: JP2011085431A

Abstract

Disclosed is a vehicle-mounted device provided with: a storage means which stores traffic information data and map data that are used for a route search; a route search means which searches for a recommended route from a departure point to a destination point on the basis of the traffic information data and map data; a data communication means which receives, from a travel characteristic data generation device, attribute-category-by-attribute-category travel characteristic data for correcting traffic information with respect to each predetermined attribute category, the attribute-category-by-attribute-category travel characteristic data being obtained by classifying travel history data relating to a plurality of other drivers according to attribute categories based on the attributes of the drivers; and a traffic information correction means which corrects the traffic information data using the attribute-category-by-attribute-category travel characteristic data relating to an attribute category corresponding to the driver of a vehicle, the route search means using the traffic information data corrected by the traffic information correction means as the traffic information data.

Description

In-vehicle device, travel characteristic data generation device, and in-vehicle information system

The present invention relates to an in-vehicle device, a travel characteristic data generation device, and an in-vehicle information system.

In-vehicle devices typified by navigation systems are required to provide route guidance that avoids delays due to traffic jams, etc., so that drivers can arrive at their destinations more quickly. The in-vehicle device performs guidance along the route searched by the Dijkstra method using traffic information, particularly travel time information for each road section (called a link) as a cost.

However, since the travel time information expresses the travel time of the average driver, the accuracy is not necessarily high depending on the travel characteristics related to the driving tendency of each driver.

Patent Document 1 is one of the prior arts that attempts to improve the accuracy of travel time information by taking into account the driving characteristics of individual drivers. In Patent Document 1, the future road conditions (travel time, etc.) of the link are estimated, and the driving speed when each driver drives the vehicle under the estimated road condition is estimated as the preferred driving speed of each driver. A technique for estimating the travel time of each link using the link distance and the estimated preference driving speed is described.

Patent Document 2 is also one of the prior arts that attempts to improve the accuracy of travel time information by taking into account the driving characteristics of individual drivers. In Patent Document 2, data obtained by measuring the traveling state of a vehicle is accumulated as traveling history information, and traffic information on the route is predicted based on the traveling history information and traffic information. A technique for predicting an arrival time at an arbitrary point is disclosed.

JP 2002-312885 A JP 2005-241519 A

However, none of the above known techniques can correct the travel time information with high accuracy in a situation where there is no driving history data of the driver or there is not a sufficient amount of driving history data. This means that when the driver uses the in-vehicle device that obtains the travel history data for the first time or when using it for the first time, none of the above-described known techniques is sufficiently effective. Furthermore, even after a lot of travel history data is acquired by the in-vehicle device, the preferred driving speed cannot be acquired for a road on which the driver has not traveled so far, so that none of the above known techniques can be applied.

The Dijkstra method described above is a method for searching for a route from a departure place to a destination based on travel time information of a road section (link). In a situation where correction is applied only to travel time information of some road sections, it is difficult to obtain a recommended route with sufficient quality by the Dijkstra method. This is because, as a road section in which travel time information is not corrected, a road section that is generally free of traffic congestion such as a residential road in a residential area or a road section with a small amount of traffic, and a route searched by the Dijkstra method, This is because it may be an unnatural route including many of these road sections.

An object of the present invention is to solve such a problem and to correct travel time information with high accuracy in consideration of estimated travel characteristics even in a situation where there is not enough travel history data for each driver. .

The vehicle-mounted device according to the first aspect of the present invention is mounted on a vehicle and stores a traffic information data and map data used for route search, and a destination based on the traffic information data and the map data. To correct the traffic information for each predetermined attribute classification, which is obtained by classifying the route search means for searching for the recommended route to the ground and the driving history data by other drivers classified for each attribute classification based on the driver attributes Communication means for receiving driving characteristic data for each attribute classification from the driving characteristic data generating device, and traffic information correction means for correcting traffic information data using the attribute characteristic driving characteristic data of the attribute classification to which the driver of the vehicle corresponds The route search means searches for a recommended route using the traffic information data corrected by the traffic information correction means.

According to the second aspect of the present invention, the in-vehicle device of the first aspect is based on a driver profile including driver attribute information, travel history data that is travel data for each driver, and travel history data based on the driver profile. Data classification means for classifying drivers for each driver, and driving characteristic data for each driver to be corrected for each attribute classification using travel history data and traffic information data classified for each driver registered in the driver profile Driving characteristic data generating means, and the traffic information correcting means corrects the traffic information data using the attribute classification-specific traveling characteristic data or the driver-specific traveling characteristic data.

According to the third aspect of the present invention, in the in-vehicle device of the second aspect, the travel characteristic data generating means is configured such that the travel history data classified for each driver is recorded more than the first predetermined number. When driving characteristic data for each driver for use in correcting traffic information data is generated, and when the driving characteristic data is less than the first predetermined number and greater than the second predetermined number, the driving characteristic data for each driver based on the driving characteristic data for each attribute classification Generate generate.

According to the fourth aspect of the present invention, in the in-vehicle device according to the third aspect, the driver profile includes the user ID of each driver, the travel history data includes the link number of the road link that has traveled, and the travel on the road link. The data classification means classifies the travel history data for each user ID based on the user ID included in the driver profile information, and the travel characteristic data generation means is classified. Based on the amount of travel history data for each user ID, a method for generating driver-specific travel characteristic data is determined.

According to the fifth aspect of the present invention, in the in-vehicle device according to the fourth aspect, the travel characteristic data generating means has the travel history data classified for each driver being less than the first predetermined number and greater than the second predetermined number. In the case, the travel time data or the average speed of each link is compared between the travel history data and the traffic information data of the driver, and a difference obtained by subtracting the traffic information data from the travel history data is obtained. The driving characteristic data for each attribute classification of the minimum attribute classification is obtained as driving characteristic data for each driver.

A driving characteristic data generating apparatus according to a sixth aspect of the present invention is a driving characteristic data generating apparatus that generates driving characteristic data for correcting traffic information data using driving history data of a plurality of drivers, and relates to driver attributes. Driver profile that includes information, travel history data that accumulates travel data for each driver, data classification means that classifies travel history data for each predetermined attribute classification based on the driver profile, and travel classified for each attribute A travel characteristic data generating means for generating travel characteristic data for each attribute classification for correcting the traffic information data for each attribute classification using the history data is provided.

According to the seventh aspect of the present invention, in the sixth driving characteristic data generation device, the driver profile includes the user ID of each driver, and at least one of birth date, gender, license acquisition date, and cumulative driving distance. The travel history data includes the link number of the travel and either the travel time or the average speed of the link, and in the data classification means, the attribute classification is information included in the driver profile and the travel history data. To be determined.

According to the eighth aspect of the present invention, in the sixth driving characteristic data generating device, the driving characteristic data generating means compares the driving history data for each attribute classification of the driver with the travel time for each link in the traffic information data, A difference obtained by subtracting the traffic information from the travel history data is obtained, and an average value of the difference is obtained as travel characteristic data for each attribute classification for correcting the traffic information data of each link for each attribute classification.

An in-vehicle information system according to a ninth aspect of the present invention includes an in-vehicle device according to any one of claims 1 to 5, a traveling characteristic data generation device according to any one of claims 6 to 8, Consists of a data communication device and a communication network that connect the travel characteristic data generation device and the in-vehicle device.

According to the present invention, even if the driving history data of the driver does not exist sufficiently, the driving characteristic data is generated by the attribute classification based on the driving history data of another driver belonging to the same attribute classification as the driver. Traffic information data can be appropriately corrected by obtaining it from the device and applying it. Further, by generating or selecting appropriate attribute classification-specific travel characteristic data according to the collection status of the driver's travel history data, the traffic information data can be appropriately corrected in consideration of the driver's driving characteristics.

This makes it possible to obtain highly accurate corrected traffic information data. And, by the shortest time route search using the corrected traffic information with high accuracy, it becomes possible to perform high-quality route guidance that avoids traffic congestion more reliably.

It is a block diagram of the vehicle-mounted information system comprised from the driving | running | working characteristic data generation apparatus of this invention, and a vehicle-mounted apparatus. It is a functional block diagram of the vehicle-mounted apparatus in 1st Embodiment of this invention. It is an example of the data format of map data. It is an example of the data format of driving history data. It is an example of the data format of a driver profile. It is an example of the data format of reference | standard traffic information data. It is an example of the data format of driving characteristic data classified by attribute classification. It is a driving characteristic data generation device side main processing flow on the driving characteristic data generation device side. It is a processing flow of the vehicle-mounted apparatus in 1st Embodiment. It is an example of the display of the display in the route | root guidance of a vehicle-mounted apparatus. It is a functional block diagram of the vehicle-mounted apparatus in 2nd Embodiment of this invention. It is a driving | running | working characteristic data generation processing flow according to attribute classification by the vehicle-mounted apparatus side in 2nd Embodiment.

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

(First embodiment)
FIG. 1 is a configuration diagram of an in-vehicle information system according to an embodiment of the present invention. The in-vehicle information system 1 in FIG. 1 includes a travel characteristic data generation device 10, an automobile 13 equipped with the in-vehicle device 11 and the data communication device 12, and a communication network 14. The in-vehicle device 11 is connected to the travel characteristic data generation device 10 via the communication network 14.

The travel characteristic data generation device 10 includes a driver attribute classification condition 100, a data classification unit 101, a travel history database 102, a travel characteristic data generation unit 103 by attribute classification, a reference traffic information database 104, a travel characteristic data 105 by attribute classification, and a driver. A profile 106 is provided. The driving characteristic data generation device 10 has a function of classifying individual drivers into attributes and generating driving characteristic data obtained by analyzing driving history data of various drivers based on the attributes.

The travel characteristic data generation device 10 is a computer device such as a personal computer (PC), a workstation, a server, a large computer, etc., and its hardware configuration includes a CPU, memory, HDD, DVD drive, display, keyboard, mouse, and the like. Composed. The travel characteristic data generation device 10 is connected to the in-vehicle device 11 via the communication network 14. The travel characteristic data generation device 10 receives travel history data for each driver from the in-vehicle device 11 and accumulates the travel history data in the travel history database 102. The travel characteristic data generation device 10 transmits the travel characteristic data classified by attribute classification from the travel characteristic data classified by attribute 105 to the in-vehicle device 11. Each part which comprises the driving | running | working characteristic data generation apparatus 10 is explained in full detail behind.

As represented by the car navigation system, the in-vehicle device 11 calculates a recommended route to the destination using map data, traffic information, and the like, and performs guidance based on the recommended route. The in-vehicle device 11 has a function of accumulating travel history data of the automobile 13. A functional block diagram of the in-vehicle device 11 is shown in FIG. In FIG. 2, the in-vehicle device 11 includes a destination setting unit 110, a traffic information correction unit 111, a reference traffic information database 112, travel characteristics data 113 by attribute classification, a route search unit 114, a search database 115, A route guidance unit 116, a travel history storage unit 117, a travel history database 118, and a map database 119 are provided. Each part shown in FIG. 2 will be described in detail later.

The in-vehicle device 11 includes, as hardware configurations, an arithmetic processing unit 40, a display 41, a data storage device 42, a voice input / output device 43, an input device 44, a wheel speed sensor 45, a geomagnetic sensor 46, a gyroscope. A sensor 47, a GPS (Global Positioning System) receiving device 48, and an in-vehicle LAN (Local Area Network) device 49 are provided. Each hardware configuration of the in-vehicle device 11 will be described in detail later.

The in-vehicle device 11 is connected to the travel characteristic data generation device 10 via the communication network 14. The in-vehicle device 11 accesses the traveling characteristic data generation device 10 in response to a request from the user via the input device 44, receives the traveling characteristic data by attribute classification from the traveling characteristic data generation device 10, and receives this by the attribute classification Stored in the travel characteristic data 113. The in-vehicle device 11 transmits the driving history data of the driver from the driving history database 118 to the driving characteristic data generation device 10.

The data communication device 12 is connected to the in-vehicle device 11 via a communication I / F (interface) 50 and includes a modem for connecting to the communication network 14. The data communication device 12 may be, for example, a communication modem that supports PHS (registered trademark) (Personal Handyphone System), DSRC (Dedicated Short Range Communication), cellular communication, short-range wireless LAN communication, or the like. It corresponds to a cellular phone or a data communication card with a built-in modem. The in-vehicle device 11 can be connected to the communication network 14 by the data communication device 12 and further connected to the traveling characteristic data generation device 10 via the communication network 14.

The hardware configuration of the in-vehicle device 11 shown in FIG. 1 will be described. The arithmetic processing unit 40 is a central unit that performs various processes. One of the processes is a positioning process for calculating the current location of the automobile 13 based on information output from various sensors such as the wheel speed sensor 45, the geomagnetic sensor 46, the gyro sensor 47, and the GPS receiver 48. Further, the arithmetic processing unit 40 is a map database in which map data necessary for displaying a map around the current location on the display 41 is stored in the data storage device 42 based on the information on the current location calculated by the positioning process. A process of reading from 119 is performed. The arithmetic processing unit 40 performs processing for displaying a mark (icon) indicating the current location together with the surrounding map on the display 41. Furthermore, the arithmetic processing unit 40 performs a process of searching for a recommended route connecting the destination set by the user and the current location (departure location) using the map database 119 or the like stored in the data storage device 42. The arithmetic processing unit 40 uses the voice input / output device 43 and the display 41 to perform processing for guiding the user along the recommended route. Each function of FIG. 2 is also processed by the arithmetic processing unit 40.

The display 41 is a unit that displays an image under the control of the arithmetic processing unit 40, and includes a CRT (Cathode Ray Ray Tube) or a liquid crystal display. Signals between the arithmetic processing unit 40 and the display 41 are generally connected by RGB signals or NTSC (National Television System Committee) signals.

The data storage device 42 includes an optical storage medium such as a CD-ROM and a DVD-ROM, a magnetic storage medium such as a hard disk, a storage medium such as a nonvolatile semiconductor memory, and a reading / writing device thereof. The data storage device 42 stores a reference traffic information database 112, attribute classification-specific travel characteristic data 113, a search database 115, a travel history database 118, and a map database 119, and a destination setting POI. Various data such as (Point of Interests) data is also stored.

An example of the map data format of the map database 119 is shown in FIG. The map data format shown in FIG. 3 is an example in the case where map data is managed as secondary mesh information that is data in units of secondary mesh, and each “secondary mesh code” that is a mesh code of a secondary mesh. The link information of each link constituting the road included in the mesh area is included. The link information includes “start point coordinates” that are the coordinate information of the start node constituting each link for each “link number”, “end point coordinates” that are the coordinate information of the end node, and road type information including each link. There is a “road type code”, “link length” which is information indicating the length of each link, “regulated speed” which is information indicating the speed limit of each link, and two nodes, a start node and an end node of each link. Each contains a pointer to the link to be connected. Here, by distinguishing the start node and the end node for the two nodes constituting the link, the upward direction and the downward direction of the same road are managed as different links.

The voice input / output device 43 converts the message to the user generated by the arithmetic processing unit 40 into a voice signal and outputs it. Further, the voice input / output device 43 recognizes the voice uttered by the user and transfers the content to the arithmetic processing unit 40. The input device 44 is a device that receives an instruction from the user, and includes a hard switch such as a scroll key or a scale change key, a joystick, a touch panel pasted on the display 41, a microphone for voice input, and the like.

The various sensors such as the wheel speed sensor 45, the geomagnetic sensor 46, and the gyro sensor 47 and the GPS receiver 48 are used by the in-vehicle device 11 to detect the current location (vehicle position). The wheel speed sensor 45 measures the travel distance from the product of the wheel circumference and the measured number of rotations of the wheel, and further measures the angle bent from the difference in the number of rotations of the paired wheels. The geomagnetic sensor 46 detects the earth's magnetic field and detects the direction in which the moving body is facing. The gyro sensor 47 is composed of an optical fiber gyro, a vibration gyro, or the like, and detects an angle of rotation of the sensor. The GPS receiver 48 receives a signal from a GPS satellite, and measures the distance between the mobile body and the GPS satellite and the rate of change of the distance with respect to three or more satellites to thereby determine the current position, traveling speed, Measure the heading and current time.

The in-vehicle LAN device 49 is a device for connecting to the in-vehicle LAN provided in the automobile 13 on which the in-vehicle device 11 is mounted. Various information flowing through the in-vehicle LAN, for example, door opening / closing information, light lighting state information , Receive engine status and failure diagnosis results.

Next, the driving characteristic data generation device 10 shown in FIG. 1 will be described.

Driver attribute classification condition 100 is a condition for classifying individual drivers into attributes based on the driving characteristics of the driver. It is desirable that drivers classified into the same attribute based on the driver attribute classification condition 100 have similar driving characteristics. The driver attribute classification condition 100 is based on factors such as the number of years that have passed since obtaining a license and the cumulative driving distance after obtaining a license, and the age at which the body's reaction speed may be affected. It is determined in consideration of factors such as weather and brightness at the driving point. For example, consider a first example in which the driving characteristics of a driver are classified by age. In the first example, based on the age of the driver, the younger age group is less than 30 years old, the attribute class is 1, 30 years old and younger than 50 years is

attribute class

2, 50 years old and younger is less than 65 years old, and the attribute classification is 3, 65. Assume that elderly people over age are divided into attribute classification 4. A rule for classifying drivers from attribute classification 1 to attribute classification 4 based on their age is the driver attribute classification condition 100. As another method of classification other than age, there may be a method related to the skill level of driving such as the number of years since the license is obtained and the cumulative driving distance after the license is obtained.

Further, the driver attribute classification condition 100 may include a gender condition that may reflect a difference in action range. For example, when the classification according to the sex condition is added to the first example, the classification is, for example, a male under 30 years old, a female under 30 years old, a male between 30 and 50 years old, a female between 30 and 50 years old, and so on. It becomes a combination. Further, the driver attribute classification condition 100 may take into account road surface conditions (dry, wet, frozen) or weather (sunny, rain, snow). For example, when the classification according to the road surface condition is added to the first example, a dry road surface under 30 years old, a wet road surface under 30 years old, a dry road surface between 30 and 50 years old, a wet road surface between 30 and 50 years old It becomes a combination of classification such as. Further, the driver attribute classification condition 100 may take into account outdoor brightness (bright, dark) and time zones (morning, noon, evening). For example, when the classification according to the outdoor brightness is added to the first example, the outdoor situation under 30 years old is bright, the outdoor condition under 30 years is dark, the outdoor condition between 30 and 50 years old is bright, 30 It is a combination of classifications such as the situation where the outdoors is over 50 years old and under 50 years old. The driver attribute classification condition 100 is stored as a data file in a data storage device such as an HDD (not shown) provided in the traveling characteristic data generation device 10.

The travel history database 102 stores travel history data collected from the travel history database 118 for each of the one or more automobiles 13 by the travel characteristic data generation device 10 via the data communication device 12 and the communication network 14. Similar to the driver attribute classification condition 100, the travel history database 102 is also stored in the data storage device. FIG. 4 shows an example of the data format of the travel history data. A method for generating the travel history data will be described in detail in the description of the travel history storage unit 117 of the in-vehicle device 11.

The driver profile 106 is data including information on individual drivers. The data of the driver profile 106 is stored as a data file in a data storage device such as an HDD in the driving characteristic data generation device 10 as with the driver attribute classification condition 100. FIG. 5 shows an example of the data format of the driver profile 106. The driver profile 106 shown in FIG. 5 is a driver ID (identical to that included in the driving history data) that is an ID for identifying each driver, the date of birth of each driver, gender, license acquisition date, cumulative driving distance. Consists of By referring to these pieces of information, each driver can be classified into attributes. The driver profile 106 is created from information provided when a user who has purchased the in-vehicle device 11 performs user registration through a website or a postcard.

The data classification unit 101 classifies the travel history data 102 into attributes based on the driver attribute classification condition 100 and the driver profile 106. Details of the processing of the data classification unit 101 will be described later together with the processing flow.

In the reference traffic information database 104, traffic information data used as a reference when judging the tendency of the attributed travel history data classified by the data classification unit 101 is stored as reference traffic information data. The reference traffic information data is, for example, travel time data for each link provided from a traffic information center, statistical travel time data obtained by statistically processing the provided link travel time according to day of the week or time zone, and the like. The reference traffic information data beta 104 is stored as a data file in a data storage device such as an HDD in the driving characteristic data generation device 10 as with the driver attribute classification condition 100. FIG. 6 shows an example of the data format of the reference traffic information data. The reference traffic information data in FIG. 6 includes a link number, a link length, and a reference travel time.

The attribute classification-specific travel data generation unit 103 compares the travel history data classified by the data classification unit 101 with the reference traffic information data having the same link number stored in the reference traffic information database 104, thereby obtaining a travel history. The tendency of the data with respect to the reference traffic information data is analyzed, and the attribute characteristic-specific traveling characteristic data 105 which is a parameter for correcting the reference traffic information data is generated.

FIG. 7 shows an example of the data format of the travel characteristic data 105 classified by attribute classification. The attribute characteristic-specific traveling characteristic data includes correction parameters for each link in each of the attributes 81 to 84 that can be classified by the driver attribute classification condition 100. The correction parameter is a value for correcting the reference traffic information data. In FIG. 7, since the reference travel time, which is the reference value in the reference traffic information data in FIG. 6, is the travel time [second] for each link, the correction parameter is also the amount [second] for correcting the travel time for each link. When the correction parameter is positive, the reference travel time increases due to the correction. If the correction parameter is negative, the reference travel time is reduced by the correction. The details of the processing in the attribute classification-specific traveling data generation unit 103 will be described later together with the processing flow of the traveling characteristic data generation device 10.

As described above, the traveling characteristic data generation device 10 generates the attribute characteristic-specific traveling characteristic data 105 which is a correction parameter for each attribute classification. This attribute classification-specific traveling characteristic data 105 is transmitted to the in-vehicle device 11 via the communication network 14. The in-vehicle device 11 receives the attribute classification-specific travel characteristic data 105 and stores it in the data storage device 42 as attribute-classification-specific travel characteristic data 113. As a result, the in-vehicle device 11 uses the attribute-specific traveling characteristic data 113 based on the traveling history data of another driver belonging to the same attribute classification as that driver for the link in which the traveling history data of the driver of the automobile 13 does not exist. By correcting the reference traffic information data, highly accurate corrected traffic information can be obtained.

Next, processing of each unit that realizes the operation of the in-vehicle device 11 illustrated in FIG. 2 will be described.

The destination setting unit 110 is an HMI (Human Machine Interface) for setting a destination by the user operating the in-vehicle device 11.

The reference traffic information database 112 is a database equivalent to the reference traffic information database 104 in the travel characteristic data generation device 10. The reference traffic information database 112 may download, for example, the reference traffic information database 104 of the travel characteristic data generation device 10 via the data communication device 12 and the communication network 14, or via FM multiplex broadcasting, DSRC or beacon communication. You may produce | generate based on the data received from the traffic information center. Alternatively, the reference traffic information data 104 held in the travel characteristic data generation device 10 may be temporarily stored in a storage medium such as a DVD-ROM and copied or moved to the in-vehicle device 11 by a reading device (not shown).

The attribute classification-specific traveling characteristic data 113 is data equivalent to the attribute classification-specific traveling characteristic data 105 in the traveling characteristic data generation apparatus 10. As the attribute characteristic-specific traveling characteristic data 113, the attribute characteristic-specific traveling characteristic data 105 may be downloaded from the traveling characteristic data generation apparatus 10 via the data communication device 12 and the communication network 14, or may be downloaded to a storage medium such as a DVD-ROM. The attribute classification-specific traveling characteristic data 105 may be temporarily stored, read by a reading device (not shown), and copied or moved to the in-vehicle device 11.

The traffic information correction unit 111 performs a process of correcting the reference traffic information data 112 using the attribute classification-specific traveling characteristic data 113.

In the search database 115, data other than the map data whose format is shown in FIG. 3 among the data used for route search and guidance is stored as search data. In the search database 115, search data is stored for each mesh code representing an area identification ID. In the search database 115, for example, information on road structures (name, type, coordinate information, etc.) other than roads included in each mesh region, information on rivers, lakes, seas, etc. in addition to green zones and mountainous areas are also included. include.

The route search unit 114 is set by the current location obtained from the GPS receiver 48 or the departure point set by the user and the destination setting unit 110 with reference to the map database 119, the search database 115, the reference traffic information database 112, and the like. Search for the optimal route (recommended route) connecting the destination. The route guidance unit 116 refers to the map database 119 and the like, and guides the user along the recommended route obtained by the route search unit 114 using the voice input / output device 43 and the display 41.

The travel history accumulating unit 117 includes various data of the map database 119, various information of the wheel speed sensor 45, the geomagnetic sensor 46, the gyro sensor 47, and the position information of the vehicle 13 measured by the GPS receiver 48, and the interior of the vehicle. Based on data from outside the in-vehicle device 11 obtained via the LAN device 49, travel history data is generated, the travel history data is stored in the travel history database 118, and output to the travel characteristic data generation device 10. The travel history data generated by the travel history storage unit 117 follows the data format shown in FIG.

The generation process of the travel history data shown in the format of FIG. 4 will be described. The data column in each row in FIG. 4 is called one record. In each record, a travel history is recorded as data for each driver, for each link traveled, and for each date and time that passed the link.

“Driver ID” is obtained by referring to the driver ID number uniquely assigned to each vehicle-mounted device 11 stored in the data storage device 42. In addition, when there are a plurality of drivers using the in-vehicle device 11 and each can be identified, an ID number corresponding to each driver may be referred to.

The “passing date and time” is the date and time when the automobile 13 passed the link, and is information in units of one second obtained from the GPS receiver 48 of the in-vehicle device 11. It is assumed that the automobile 13 passes the link when the automobile 13 passes either the link start node or the end node. It is unified for all records whether the start node or the end node is considered to have passed the link. In the following, the date and time when the link has passed the start node is recorded.

The “link number” is the current location of the vehicle 13 obtained from the latitude / longitude information obtained at a frequency of once or less per second from the wheel speed sensor 45, the geomagnetic sensor 46, the gyro sensor 47, and the GPS receiver 48 of the in-vehicle device. The position information is specified by matching each coordinate in the link including the start node and the end node of the link included in the map database 119 stored in the data storage device 42.

"Link length" is included in the map data and can be referenced using the corresponding link number as a key.

The “link travel time” can be calculated from the difference between the end node passage date and time and the start node passage date and time by specifying the passage date and time of the start node and end node of the link. This value is also data in units of one second like the passage date and time. Instead of the link travel time, the link average speed obtained using the link travel time and the link length may be recorded in the travel history data.

“Road surface condition” and “outdoor brightness” are values obtained from the outside of the in-vehicle device 11 via the in-vehicle LAN device 49 of the in-vehicle device 11. The road surface state is recognized from an in-vehicle camera and an image recognition device provided in the automobile 13, and outdoor brightness is obtained from an illuminance sensor provided in the automobile 13.

The travel history database 118 that accumulates the travel history data of the automobile 13 in record units in this way is collected in the travel characteristic data generation device 10 via the data communication device 12 and stored as the travel history database 102.

Next, processing of the travel characteristic data generation apparatus 10 according to the present invention will be described with reference to FIG.

First, the driving characteristic data generation device 10 reads a data file of the driver attribute classification condition 100 from a data storage device (not shown). The driving characteristic data generation device 10 reads the driver profile 106 from the data storage device. The travel characteristic data generation device 10 reads travel history data from the travel history database 102 and reads reference traffic information data from the reference traffic information database 104 (step S51).

Next, the travel characteristic data generation device 10 classifies the travel history data read in step S51 by the data classification unit 101 based on the driver attribute classification condition 100 and the driver profile 106 read in step S51. Here, the driver attribute classification condition 100 is the above-mentioned younger age group under 30 years old, the attribute class 1, 30 years old and younger than 50 years

old attribute class

2, 50 years old and younger than 65 years old attribute class 3, 65 years old and older An example of using an age-based classification rule that divides the demographic group into attribute classification 4 will be described. At this time, if the driver profile 106 has the contents shown in FIG. 5 and the travel history data 102 is the data shown in FIG. 4, the date of birth and current date and time (2009) of the drivers whose driver IDs are 1 and 2 The driver's age is 33 years old and 30 years old, and both belong to attribute classification 2 because they are both 30 years old and younger than 50 years old. Therefore, all the travel history data related to the drivers with

driver IDs

1 and 2 are classified as belonging to attribute classification 2. As described above, the travel characteristic data generation device 10 classifies all the records in the travel history database 102 into attributes based on the driver attribute classification condition 100 (step S52).

Next, the travel characteristic data generation device 10 compares all records in the travel history database 102 classified into attributes with the reference traffic information data read in step S51 in the attribute classification-specific travel characteristic data generation unit 103. Thus, the tendency of each record with respect to the reference traffic information data is analyzed, and a correction parameter is generated. The correction parameters are stored as attribute classification-specific traveling characteristic data 105 (step S53).

Here, when all records in the travel history database 102 are classified as shown in FIG. 4 by the data classification unit 101 and the reference traffic information data has the values shown in FIG. And the reference travel time are compared, it can be seen that the link travel time in FIG. 4 is shorter than the reference travel time in FIG. If this is converted into speed, it can be seen that the driving of each driver remaining in the travel history data of FIG. 4 tends to travel at a higher speed than the reference traffic information data 104 of FIG.

By such trend analysis, the travel time Tb of the reference traffic information is subtracted from the link travel time Th of the travel history data for all data of the same attribute classification in the same link, as shown in Equation 1 below. The average value Ta of each difference (Th−Tb) is used as a correction parameter for correcting the reference traffic information data.

In Expression 1, i is a data suffix in a certain link, and n is the number of data of a link to be processed.

In this example, the attribute classification travel characteristic data generation unit 103 obtains the correction parameter using the travel time of the link, such as the travel time of the link history of the travel history data and the reference travel time of the reference traffic information data. When the average speed at the link is used as the reference value as the traffic information data, the average speed for each link is used instead of the travel time in Equation 1.

Further, in the example described here, the correction parameter Ta is once generated for each link, but the parameters generated for each link are aggregated in a predetermined unit such as an area or a road type, and an average value thereof is calculated for each aggregation unit. May be used as a correction parameter. For example, in the map data in the format shown in FIG. 3, an area-related code (secondary mesh code) and road type (highway, general road, etc.) are associated with each link. The classified travel characteristic data generation unit 103 can calculate a correction parameter for each aggregation unit.

Next, the processing of the in-vehicle device 11 according to the present invention will be described with reference to FIG.

First, in the in-vehicle device 11, the destination is set by the HMI operation on the voice input / output device 43 or the input device 44 by the user in the destination setting unit 110 (step S 61). Here, the destination is a method of inputting a cursor moved on the map display screen as a destination, a method of selecting from a list of destinations (POI) after selecting a genre in a destination search menu, or a telephone number. This is set by a method of setting a destination by a search specifying an address or the like.

Next, the in-vehicle device 11 reads the reference traffic information data from the reference traffic information database 112 in the traffic information correction unit 111, and reads the travel characteristic data 113 by attribute classification from the data storage device 42 (step S62). And the vehicle equipment 11 correct | amends the traffic information of the said reference | standard traffic information data in the traffic information correction | amendment part 111 using the said attribute classification driving | running | working characteristic data 113 (step S63).

A process in which the traffic information correction unit 111 corrects the reference traffic information data in the reference traffic information database 112 will be described. Assuming that the correction parameter for each driver attribute classification based on the driving characteristic data 113 according to attribute classification is Ta and the reference traffic information data based on the reference traffic information database 112 is Tb, the corrected reference traffic information data Tb ′ is expressed by the following (Equation 2). It is expressed as

In this way, even if there is not enough driving history data for the target driver, if there is enough driving history data collected from another driver belonging to the same attribute classification as the driver, the driving history data is based on the driving history data. The traffic parameter Tb based on the reference traffic information data held in the reference traffic information database 112 by the traffic information correction unit 111 is obtained by obtaining the correction parameter Ta in the travel characteristic data 105 according to the attribute classification from the travel characteristic data generation device 10. The corrected traffic information data Tb ′ with high accuracy can be obtained.

Here, in this example, the correction parameter Ta in the attribute classification-specific travel characteristic data 113 is described as being generated for each link, but as described above, the attribute characteristic-specific travel characteristic data generated for each link. 113 may be aggregated in a predetermined unit such as area or road type, and an average value thereof may be used as a correction parameter in driving characteristic data by attribute classification for each aggregation unit. In this case, the correction obtained for each aggregation unit Parameters (travel characteristic data) can be collectively applied to the reference traffic information data of the links corresponding to the respective aggregation units.

Next, the in-vehicle device 11 uses the route searching unit 114 to determine the position information measured by the wheel speed sensor 45, the geomagnetic sensor 46, the various sensors of the gyro sensor 47, and the GPS receiver 48 of the in-vehicle device 11 as described above. Referring to the search data 115, an optimum route (recommended route) that connects the current location of the vehicle 13 obtained from each of the data, or the starting point set by the user and the destination set by the destination setting unit 110 is referred to. ) Is searched (step S64). At this time, using the corrected traffic information data Tb ′, the shortest time route when the travel time is the cost of the link is calculated as a recommended route by the Dijkstra method.

Next, the in-vehicle device 11 creates guidance data necessary for guidance from the map data stored in the data storage device 42 based on the recommended route information searched by the route search unit 114 in the route guidance unit 116. This guidance data includes major intersections, intersections with complex structures, or enlarged views of intersections that need to turn right or left (referred to as guidance intersections), and detailed route information from entry to exit of the guidance intersection, Or the distance information etc. to the guidance intersection are included. Then, based on the created guidance data, route guidance is performed according to the current location that is sequentially obtained (step S65).

For example, in step S65, the in-vehicle device 11 displays the distance from the current location to the nearest guidance intersection on the display 41 or notifies by voice. In addition, if there is a guided intersection within a predetermined distance from the current location, the in-vehicle device 11 sequentially updates the distance to the guided intersection displayed on the display 41, and "the intersection is left in the future 500m" Is guided in the direction of travel. Further, the in-vehicle device 11 calculates the total travel time to the destination by calculating the sum of the travel time of each link constituting the calculated recommended route. And the vehicle equipment 11 calculates the arrival time to the destination from the present time and the total travel time. Here, there are two types of travel time for each link: before the correction, that is, the travel time of the reference traffic information data, and the travel time after the correction. However, when the travel time after correction does not exist, the travel time before correction is substituted. Therefore, the in-vehicle device 11 calculates a value based on two types of travel times before and after the correction for the estimated arrival time at the destination.

FIG. 10 is a display example of the display 41 at the time of route guidance in step S65. In this figure, 90 indicates the current position, 91 indicates the current date and time, 92 indicates the destination set by the user in the

destination setting unit

110, and 93 indicates the recommended route calculated by the route search unit 114. 94 is the estimated arrival time at the destination. Among them, “standard” is a value before correction, that is, a value calculated based on the reference traffic information data, and “correction” is corrected by the travel characteristic data 113 by attribute classification. It is a value calculated based on the traffic information. Thereby, the user can know what the correction result by the attribute classification of the driver is compared with the reference value.

Next, the in-vehicle device 11 uses the travel history storage unit 117 to store various data such as map data stored in the data storage device 42 and various sensors such as the wheel speed sensor 45, the geomagnetic sensor 46, and the gyro sensor 47 as described above. The travel history data is generated by measuring and processing the travel data of the vehicle from the value measured during the travel and the positioning information measured by the GPS receiver 48 to generate travel history data. (Step S66). The travel history data accumulated in the travel history database 118 is transmitted to the travel characteristic data generation device 10 via the communication network 14. It should be noted that, since the vehicle travel data can be measured and processed after the engine of the automobile 13 is started and the in-vehicle device 11 is started, the process of the travel history accumulating unit 117 is the process from step S61 to S65. Regardless, it is executed periodically.

As a result, the in-vehicle device 11 uses the attribute characteristic-specific traveling characteristic data 105 generated from the traveling history data of another driver belonging to the same attribute classification as the driver even when the driving history data of the driver does not exist, as the driving characteristic data generation device. 10 and the reference traffic information data 112 is corrected to obtain highly accurate corrected traffic information. Then, by the minimum cost route search using the corrected traffic information with high accuracy, it becomes possible to perform high-quality route guidance that avoids traffic congestion more reliably.

(Second Embodiment)
In the first embodiment, the in-vehicle device 11 has a driving characteristic by attribute classification based on the driving history data of another driver belonging to the same attribute classification as the driver even when the driving history data of the driver is not sufficiently present. The accuracy of the traffic information is improved by applying the data 105 and correcting the reference traffic information data in the reference traffic information database 112. This is effective in situations where the driver has just started using the vehicle-mounted device 11 that acquires travel history data for the first time or when using it for the first time.

This idea is based on the idea that the driving characteristics of a driver are similar to the driving characteristics of another driver belonging to the same attribute classification. However, since the driving characteristics of the driver do not necessarily match, In a situation where the driving history data of the driver is sufficiently present after repeated use, it is considered that the accuracy is higher when the reference traffic information data is corrected using the driving history data of each driver.

Therefore, in the second embodiment of the present invention, when there is a travel history data exceeding a predetermined amount by the driver, the reference traffic information data of all roads including the untraveled road is corrected based on the respective travel history data. To consider the driving characteristics of the driver.

The traveling characteristic data generation device 10 in the second embodiment of the present invention is the same as that in the first embodiment.

However, the in-vehicle device is different from the first embodiment. FIG. 11 shows a functional block diagram of the in-vehicle device 1011 in the second embodiment. As shown in the functional block diagram of FIG. 11, the in-vehicle device 1011 generates the driving characteristic data of the driver based on the accumulated driving history data for each driver in addition to the functions of the in-vehicle device 11 described in the first embodiment. It has a function. For this reason, the in-vehicle device 1011 has a configuration in which attribute-specific traveling characteristic data 120, a driver profile 121, a data classification unit 122, and a driver-specific traveling characteristic data generation unit 123 are added to the in-vehicle device 11.

Next, processing of each part constituting the in-vehicle device 1011 in FIG. 11 will be described. In addition, description about the same part as the vehicle-mounted apparatus 11 in 1st Embodiment is abbreviate | omitted.

The attribute characteristic-specific travel characteristic data 120 is a parameter for correcting the reference traffic information for each attribute class as in the case of the attribute characteristic-specific travel characteristic data 105 in the travel characteristic data generation device 10, but here, the travel history for each individual driver. It also includes correction parameters generated from the data. The attribute classification-specific travel characteristic data 120 is obtained from the travel characteristic data generation device 10 as in the first embodiment, and also stores correction parameters generated by the driver-specific travel characteristic data generation unit 123.

The driver profile 121 is data including information on individual drivers, like the driver profile 106 in the travel characteristic data generation device 10 of the first embodiment. Note that in the second embodiment, it is sufficient that the driver can be identified, so there is no problem even if there is no information other than the driver ID.

The data classification unit 122 performs processing for classifying the travel history data 118 based on the driver ID registered in the driver profile 121. Further, a process for determining the amount of travel history data for each classified driver ID by a predetermined method is also performed. This processing result is used in the driver-specific travel characteristic data generation unit 123. Details of the processing will be described later together with the processing flow.

The driving characteristic data generation unit 123 for each driver compares the driving history data for each driver ID classified by the data classification unit 122 with the reference traffic information data 112 or the driving characteristic data 120 for each attribute classification, thereby The tendency with respect to the reference traffic information data 112 is analyzed, and the attribute characteristic-specific traveling characteristic data 120 that is a parameter for correcting the reference traffic information data 112 is generated and output. The data format of the attribute characteristic-specific traveling characteristic data 120 is the same as that in the first embodiment. Details of the processing will be described later together with the processing flow.

Next, with reference to FIG. 12, a description will be given of the generation processing of attribute-specific traveling characteristic data 120 in the in-vehicle device 1011 according to the present invention.

First, the in-vehicle device 1011 uses the data classification unit 122 to select a driver from the driver profile 121 that should generate the attribute characteristic-specific traveling characteristic data 120 (step S71). The driver may be selected by the user by the HMI from the driver list displayed on the display 41 of the in-vehicle device 1011 or automatically selected in the ascending order of the driver ID registered in the driver profile 121. Also good. When the driver is not registered in the driver profile 121, it is assumed that the user newly registers by the HMI displayed on the display 41.

Next, the in-vehicle device 1011 reads various data necessary for processing, such as the reference traffic information data 112, the travel history data 118, and the travel characteristic data 120 by attribute classification, from the data storage device 42 (step S72).

Next, the in-vehicle device 1011 identifies the driver ID of the user profile 121 for the driver selected in step S71. The in-vehicle device 1011 searches the travel history data 118 and extracts a data record corresponding to the driver ID (step S73). For example, when a driver whose driver ID is “1” is selected, a record of data whose driver ID is “1” is stored when the record of the driving history shown in FIG. (First two lines of data) are extracted.

Next, the in-vehicle device 1011 analyzes the amount of travel history data extracted in the process of step S73. First, the in-vehicle device 1011 obtains the number of records of the travel history data extracted in the process of step S73. Next, the in-vehicle device 1011 compares the number of records with two threshold values (ε1, ε2), and determines the value of the data amount determination value. The threshold values ε1 and ε2 are both positive numbers and have a relationship of ε1> ε2. If the number of records is greater than ε1, it is determined that there is a sufficient amount of travel history data for the driver selected in step S71, and 1 is given as the data amount determination value. If the number of records is equal to or less than ε1 and greater than ε2, it is determined that there is not a sufficient amount of travel history data for the driver selected in step S71, but a minimum amount of travel history data exists. Given. If the number of records is ε2 or less, it is determined that there is no minimum amount of travel history data for the driver selected in step S71, and 3 is given as the data amount determination value (step S74).

Here, although the method of determining based on the number of records extracted in the process of step S73 is illustrated, classification may be performed for each item included in the record, and determination may be made based on the number of each classification. For example, in the case of the data format of the travel history data shown in FIG. For example, when classified by “road surface condition”, it can be classified into “dry”, “wet”, and “freeze”. In addition, when classified according to “outdoor brightness”, it can be classified into “bright” and “dark”. Here, in the example in which the records are separated by the value of “road surface condition”, the number of records extracted in the process of step S73 is the number of “dry” records, the number of “wet” records, and the number of “freeze” records. And the number of records of each classification is compared with the above-described threshold value to determine the data amount determination value. In this case, depending on the travel history data, the data amount determination values for all road surface conditions may be the same or different data amount determination values. The same applies to the case where the records extracted in step S73 are classified by the value of “outdoor brightness”.

The in-vehicle device 1011 proceeds to the processing of step S75 because the travel history data amount is sufficient when 1 or 2 is given as the data amount determination value in the processing of step S74, and 3 is given as the data amount determination value. Sometimes it is determined that the amount of travel history data is not sufficient, and the process is terminated. The same applies when the data amount determination value is given for each value of “road surface condition” or “outdoor brightness”.

Next, the in-vehicle device 1011 proceeds to the processing of the driver-specific travel characteristic data generation unit 123. In the process of step S74, when 1 is given as the data quantity determination value, the driving characteristic data generation method 1 for each driver is selected, and when 2 is given as the data quantity determination value, the driving characteristic data generation method 2 by driver is selected. Then, the driving characteristic data generation method for each driver to be executed in step S76 is determined (step S75). The driver-specific travel characteristic data generation method 1 and the driver-specific travel characteristic data generation method 2 will be described in detail later.

Next, the in-vehicle device 1011 generates a correction parameter for the corresponding driver according to the driving characteristic data generation method for each driver determined in step S75, and adds it to the attribute characteristic-specific driving characteristic data 120 (step S76).

Here, examples of the driving characteristic data generation method 1 for each driver and the driving characteristic data generation method 2 for each driver will be described. In the driving characteristic data generation method 1 for each driver, since a sufficient amount of driving history data exists for the driver selected in step S71, the driving characteristics of the driver are sufficiently reflected. That is, the correction parameter Ta is calculated based on Equation 1, where Th is the link travel time in the travel history data of the driver and Tb is the reference travel time of the reference traffic information data. The Ta is an average value of the difference between the driving history data of the driver and the reference traffic information data, and is a value representing the driving characteristics of the driver.

In the driving characteristic data generation method 2 by driver, since the driver selected in step S71 is not sufficient but there is a minimum amount of driving history data, the driving characteristic of the driver should be sufficiently reflected. However, the driving characteristic data by attribute classification having the closest attribute to the driving characteristic of the driver is selected. Specifically, first, the correction parameter Ta is once calculated for each link using Equation 1 with Th as the link travel time in the travel history data of the driver and Tb as the reference travel time in the reference traffic information data. Operating characteristics. Next, reference is made to the attribute classification-specific traveling characteristic data Ta ′ _j for each attribute classification already stored in the attribute classification-specific traveling characteristic data 120. Here, as described in the first embodiment, Ta ′ _j is a correction parameter for each link generated by the travel characteristic data generation device 10 from, for example, the travel history data of the driver of the age group j. Next, the degree of deviation δ _j between the attribute classification j and the driving characteristics of the driver is calculated according to the following expression 3. Here, k is a link that exists in common in Ta and Ta ′ _j , and m is the number of data of all the links that exist in common.

The in-vehicle device 1011 obtains the divergence degree for all the age groups j, and among them, the attribute classification of the age group J indicating the minimum value of the divergence degree is regarded as the closest driving characteristic for this driver. Accordingly, in this case, Ta ′ _J is the attribute characteristic-specific traveling characteristic data to be selected.

Here, in this example, it is described that the attribute characteristic-specific traveling characteristic data Ta and Ta ′ _J are once generated for each link. However, as described in the first embodiment, they are generated for each link. The attribute characteristic-specific travel characteristic data may be aggregated in a predetermined unit such as an area or road type, and the average value thereof may be used as attribute classification-specific travel characteristic data of each aggregation unit. In this case, correction parameters (driver-specific travel characteristic data) obtained in each aggregation unit can be collectively applied to the traffic information of links belonging to the aggregation unit.

Next, the in-vehicle device 1011 determines whether all users registered in the driver profile 121 have been processed (step S77). If all the users have not been processed, the process returns to step S71 to perform the process. If the processing for all users has been completed, this processing ends.

As described above, the in-vehicle device 1011 can calculate the correction parameter representing the driving characteristic of the driver of the automobile 13 as the driving characteristic data for each driver, and updates the driving characteristic data 120 for each attribute classification with the correction parameter. be able to. Also, by referring to the updated attribute classification-specific traveling characteristic data 120, the reference traffic information data 112 can be more appropriately corrected as in the first embodiment, and more appropriately connecting the departure place and the destination. The recommended route can be calculated.

As described above, according to the second embodiment of the present invention, driving characteristics data for each attribute classification is generated or selected according to the collection status of the driving history data of the driver, and the driving characteristics of each driver are taken into consideration. It is possible to correct the reference traffic information data in the reference traffic information database 112, thereby obtaining highly accurate corrected traffic information data. And, by the shortest time route search using the corrected traffic information with high accuracy, it becomes possible to perform high-quality route guidance that avoids traffic congestion more reliably.

As described above, the driving characteristic data generation device, the in-vehicle device, and the in-vehicle information system according to the present invention improve the accuracy of traffic information by correcting the traffic information data in consideration of the driving characteristics of individual drivers. For example, for a vehicle navigation system such as a car navigation system installed in an automobile, or a route guidance system including an in-vehicle apparatus and a server. The present invention can be applied.

DESCRIPTION OF SYMBOLS 10 Running characteristic

data generation apparatus

11, 1011 In-vehicle apparatus 12 Data communication apparatus 13 Car 14 Communication network 40 Operation processing part 41 Display 42 Data storage apparatus 43 Voice input / output apparatus 44 Input apparatus 45 Wheel speed sensor 46 Geomagnetic sensor 47 Gyro sensor 48 GPS Receiver 49 Vehicle LAN device 50 Communication I / F
101, 122

Data classification unit

102, 118 Travel history database 103 Attribute classification-specific travel characteristic

data generation unit

104, 112 Reference

traffic information database

105, 113, 120 Attribute classification-specific travel

characteristic data

106, 121 Driver profile 111 Traffic information correction unit 123 Driver-specific travel characteristics data generator

Claims

A storage means mounted on the vehicle for storing traffic information data and map data used for route search;
A route search means for searching for a recommended route from the departure point to the destination based on the traffic information data and the map data;
Generate travel characteristic data by attribute classification for correcting the traffic information for each predetermined attribute classification, which is obtained by classifying the driving history data by other drivers for each attribute classification based on the driver attributes. Data communication means for receiving from the device;
Traffic information correction means for correcting the traffic information data using attribute classification-specific travel characteristic data of the attribute classification to which the driver of the vehicle corresponds,
The route search means is an in-vehicle device that searches for a recommended route using the traffic information data corrected by the traffic information correction means.
The in-vehicle device according to claim 1,
A driver profile that contains driver attribute information,
Driving history data that is driving data for each driver,
Data classification means for classifying the travel history data for each driver based on the driver profile;
Driving characteristic data generating means for generating driving characteristic data for each driver for correcting for each attribute classification using the driving history data and the traffic information data classified for each driver registered in the driver profile;
Further comprising
The traffic information correction means corrects the traffic information data using the attribute classification-specific travel characteristic data or the driver-specific travel characteristic data.
The in-vehicle device according to claim 2,
When the travel history data classified for each driver is recorded more than the first predetermined number, the travel characteristic data generation means generates driver-specific travel characteristic data for use in correcting the traffic information data. If it is generated and is greater than or equal to the second predetermined number and less than the first predetermined number, generation of driver-specific traveling characteristic data is generated based on the attribute classification-specific traveling characteristic data.
The in-vehicle device according to claim 3,
The driver profile includes a user ID of each driver,
The travel history data includes a link number of a road link that has traveled, and either one of travel time or average speed on the road link,
The data classification means classifies the travel history data for each user ID based on the user ID included in the driver profile information,
The driving characteristic data generating means determines a method of generating the driving characteristic data for each driver based on the amount of driving history data for each classified user ID.
The in-vehicle device according to claim 4,
The running characteristic data generating means includes
When the travel history data classified for each driver is less than or equal to the first predetermined number and greater than the second predetermined number, the travel time or average for each link for the travel history data and the traffic information data of the driver The speed is compared, the difference obtained by subtracting the traffic information data from the travel history data is obtained, and the attribute characteristic travel characteristic data of the attribute class that minimizes the difference from the difference is used as the driver characteristic travel characteristic data. Ask.
A driving characteristic data generating device that generates driving characteristic data for correcting traffic information data using driving history data of a plurality of drivers,
A driver profile that contains information about the attributes of the driver, and
Driving history data that accumulates driving data for each driver,
Data classification means for classifying the travel history data for each predetermined attribute classification based on the driver profile;
A travel characteristic data generating device comprising travel characteristic data generating means for generating attribute characteristic-specific travel characteristic data for correcting the traffic information data for each attribute class using the travel history data classified for each attribute.
In the travel characteristic data generation device according to claim 6,
The driver profile includes a user ID of each driver, and further includes at least one information of date of birth, sex, date of license acquisition, cumulative driving distance,
The travel history data includes a traveled link number and either the travel time or the average speed of the link,
In the data classification means, the attribute classification is determined based on information included in the driver profile and the travel history data.
In the travel characteristic data generation device according to claim 6,
The running characteristic data generating means includes
The travel history data for each attribute classification of the driver is compared with the travel time for each link in the traffic information data, a difference obtained by subtracting the traffic information from the travel history data is obtained, and an average value of the differences is obtained. The traffic information data of each link is obtained as attribute characteristic-specific travel characteristic data for correcting for each attribute class.
The in-vehicle device according to any one of claims 1 to 5,
The travel characteristic data generation device according to any one of claims 6 to 8,
An in-vehicle information system including a data communication device and a communication network that connect the travel characteristic data generation device and the in-vehicle device.