JP2011085431A - Device for generating travel-characteristic-data, onboard device, and onboard information system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To acquire high accuracy traffic information by properly correcting traffic information, even when a sufficient amount of travel history data concerning a certain driver does not exist. <P>SOLUTION: An onboard device includes: a driver profile containing information on attributes of drivers; travel history data being data on the results of travelling performed by the drivers; a data classification means for classifying the travel history data for each attribute classification of the drivers based on the driver profile; and a travel-characteristic-data generation means for generating travel characteristic data classified by each attribute classification for correcting traffic information data for each attribute classification by using the travel history data classified for each attribute classification of the drivers. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a technique for improving route quality by correcting traffic information used for route calculation.

  An in-vehicle device represented by a navigation system is required to provide route guidance that avoids delays due to traffic jams so that it can arrive at a destination earlier. Specifically, traffic information, particularly travel time information for each road section (called a link) is used as a cost, and a route searched by a minimum cost route search method called a Dijkstra method is guided.

  However, since the travel time information used here represents the average driver's travel time, it may not be accurate for each driver depending on the driving characteristics related to the driver's driving tendency, such as driving safety and impatient driving. Was not good.

  JP-A-2002-312885 estimates the future road conditions (travel time, etc.) of links as a conventional technique for improving the accuracy of travel time information by taking into account the driving characteristics of individual drivers. Then, the driving speed when each driver drives the vehicle under the estimated road condition is estimated as the preference driving speed of each driver, and the required distance for each link is calculated using the link distance and the estimated preference driving speed. Techniques for estimating time are described.

  Further, as another conventional technique for improving the accuracy of travel time information by considering the driving characteristics of individual drivers, Japanese Patent Laid-Open No. 2005-241519 discloses data obtained by measuring the driving state of a vehicle. A technique is disclosed that predicts arrival time at an arbitrary point on a route including a destination by accumulating it as history information and predicting traffic information on the route based on the travel history information and traffic information.

JP 2002-312885 A JP 2005-241519 A

  However, any of the above known techniques has the following problems. That is, travel time information cannot be corrected with high accuracy in a situation where there is no travel history data of a certain driver or there is no sufficient amount of travel history data. This means that when the driver uses the vehicle-mounted device for acquiring the travel history data for the first time or when using it for the first time, none of the above-mentioned known techniques is sufficiently effective. Furthermore, even after a lot of travel history data is acquired by the in-vehicle device, it cannot be applied to a road where the driver has not traveled so far because the preferred driving speed cannot be acquired.

  In addition, the Dijkstra method, which is the minimum cost route search method described above, is a method that basically searches and calculates the travel time of all road sections (links) from the departure point to the destination without any difficulty. In a situation where correction is applied only to road travel time information, it is difficult to obtain a recommended route of sufficient quality. This is because roads for which travel time information is not corrected include roads where there is generally no traffic jam, such as residential roads in residential areas, and roads with low traffic volumes, and tend to select such roads as routes. This is because a high unnatural route is often calculated.

  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.

  Even in situations where there is not enough driving history data for the driver, it is possible to correct the travel time information with high accuracy in consideration of the estimated driving characteristics, and appropriate driving according to the amount of driving history data of the driver In order to make it possible to generate characteristic data and correct travel time information with high accuracy, the driving characteristic data generation device of the present invention relates to driving history data that is data obtained as a result of driving by a driver, and driver attributes. A driver profile including information, a data classification means for classifying the driving history data for each attribute classification based on the driver profile, and traffic information data for each attribute classification using the driving history data classified for each driver attribute. A driving characteristic data generating means for generating driving characteristic data for each attribute classification for correction; That.

  The in-vehicle device of the present invention includes means for inputting a destination, traffic information for searching for a recommended route, traveling characteristic data for each attribute classification for correcting the traffic information for each attribute classification, and for each attribute classification. Traffic information correction means for correcting traffic information using travel characteristic data, route search means for searching for a recommended route to the destination using the corrected traffic information, and guidance information is generated and guided according to the searched recommended route Route guidance means for guiding.

  The in-vehicle device of the present invention further includes a driver profile that includes information related to driver attributes, travel history data that is a result of traveling by the driver, and travel history data based on the driver profile for each attribute classification. Data classification means for classifying and driving characteristic data generating means for generating driving characteristic data for each attribute classification for correcting traffic information data for each attribute classification using driving history data classified for each attribute of the driver.

  Moreover, the vehicle-mounted information system of this invention is provided with the data communication apparatus and communication network which connect a driving | running | working characteristic data generation apparatus, a vehicle-mounted apparatus, a driving | running | working characteristic data generation apparatus, and a vehicle-mounted apparatus.

  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.

  As a result, highly accurate corrected traffic information data can be obtained. 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.

  Embodiments of the present invention will be described below with reference to the drawings.

(First embodiment)
FIG. 1 shows a configuration of an in-vehicle information system including a travel characteristic data generation device 10 and an in-vehicle device 11 according to an embodiment of the present invention. This in-vehicle information system includes a travel characteristic data generation device 10, an in-vehicle device 11, a data communication device 12, an in-vehicle device 11, an automobile 13 equipped with 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 driving characteristic data generation apparatus 10 using the present invention includes a driver attribute classification condition 100, a data classification unit 101, a driving history data 102, a driving characteristic data generation unit 103 by attribute classification, a reference traffic information data 104, a driving characteristic by attribute classification. It has data 105 and a driver profile 106, and has a function of classifying individual drivers and generating driving characteristic data by analyzing driving history data of various drivers based on the classification.

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

  As represented by the car navigation system, the in-vehicle device 11 calculates the shortest time route to the destination using traffic information or the like, and performs guidance guidance based on the route obtained from the calculation result. 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 in the present embodiment is shown in FIG. The in-vehicle device 11 includes a destination setting unit 110, a traffic information correction unit 111, reference traffic information data 112, attribute-specific travel characteristic data 113, a route search unit 114, search data 115, a route guidance unit 116, and a travel history storage unit 117. , And travel history data 118.

  The in-vehicle device 11 is connected to the travel characteristic data generation device 10 via the communication network 14, and accesses the travel characteristic data generation device 10 in response to a request from the user via the input device 44. Receives the attribute-specific traveling characteristic data 115 from the storage and stores it. Then, the collected driving history data 118 of the driver is transmitted to the driving characteristic data generation device 10. Each part which comprises the vehicle equipment 11 is explained in full detail behind.

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. Specific examples include PHS (Personal Handyphone System), DSRC (Dedicated Short Range Communication), other communication modems that support cellular communication or short-range wireless LAN communication, or such communication modems. It corresponds to a mobile phone or a data communication card. 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.

  As shown in FIG. 1, the in-vehicle device 11 includes 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 gyro sensor 47, GPS ( The hardware configuration includes a global positioning system (receiver) 48 and an in-vehicle LAN (local area network) device 49.

  The arithmetic processing unit 40 is a central unit that performs various processes. One of the processes is a positioning process for obtaining the current position based on information output from various sensors such as the wheel speed sensor 45, the geomagnetic sensor 46, and the gyro sensor 47 and the GPS receiver 48. Further, based on the obtained current location information, a process of reading out map data necessary for display on the display 41 from the data storage device 42 is performed. The read map data is developed in graphics, and a mark (icon) indicating the current location is superimposed on the map data and displayed on the display 41. Furthermore, using the map data 119 and the like stored in the data storage device 42, a recommended route connecting the destination set by the user and the current location (departure location) is searched, and the voice input / output device 43 and the display 41 are used. Then, the guidance guidance along the recommended route is performed to the user. Each function of FIG. 2 described later including these functions is a function realized by the arithmetic processing unit 40.

The display 41 is a unit that displays the graphics information generated by the arithmetic processing unit 40, and includes a CRT (Cathode 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 for these storage media. This storage medium stores various data such as map data, POI (Point of Interests) data for destination setting, and search data for route search.

  The format of the map data 119 stored in the data storage device 42 is shown in FIG. This figure shows an example in which the map data 119 is managed as secondary mesh information that is data in units of secondary meshes. For each “secondary mesh code” that is a mesh code of the secondary mesh, the mesh is displayed. It contains link information for each link that makes up the road included in the area. The link information is “start point coordinates” and “end point coordinates” which are coordinate information of two nodes of a start node and an end node constituting a link for each “link number”, and road type information including a link “ “Road type code”, “Link length” which is information indicating the length of the link, “Regulated speed” which is information indicating the speed limit of the link, and the link nodes connected to the two nodes of the link start node and end node, respectively. Includes pointers. 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. In addition, processing for recognizing the voice uttered by the user and transferring the contents to the arithmetic processing unit 40 is performed. The input device 44 is a device that receives an instruction from the user, and includes a hardware switch such as a scroll key and a scale change key, a joystick, a touch panel pasted on the display 41, a microphone for voice input, and the like.

  Various sensors such as a wheel speed sensor 45, a geomagnetic sensor 46, and a gyro sensor 47 and a GPS receiver 48 are used by the in-vehicle device 11 to detect the current location (own 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 travel characteristic data generation device 10 of FIG. 1 will be described.

  The driver attribute classification condition 100 is a condition for classifying individual drivers by their attributes from the viewpoint of running characteristics. Here, it is desirable that the driving characteristics of the drivers within the same attribute classification are similar. Therefore, the number of years after obtaining a license, the cumulative driving distance after obtaining a license, the age at which the body's reaction speed may be affected, the weather and brightness of the driving point, which are likely to be related to driving familiarity / experience These factors are taken into account when classifying driving characteristics. For example, consider a first example in which travel characteristics are classified by age. In this case, the younger age group of 30 years old is divided into the attribute classification 1, the 30 year old and younger than 50 year old attribute class 2, the 50 year old and younger than 65 year old attribute class 3, and the elderly group 65 and older is divided into the attribute class 4. Then, this classification rule becomes a driver attribute classification condition. As another method of classification other than the age, the number of years after obtaining the license, the cumulative driving distance after obtaining the license, or the like may be related to the driving skill.

  In addition, the classification method may include a sex condition that may reflect a difference in behavior range. In the first example, a male under 30 years old, a female under 30 years old, 30 It is a combination of classifications such as males aged between 50 and 50, females aged between 30 and 50, and so on. In addition, road classification (dry, wet, frozen) or weather (sunny, rain, snow) may be added to the classification method, and an example of classification according to the road condition using the first example is as follows: It is a combination of classifications such as 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, and so on. In addition, outdoor classification (bright, dark) and time zone (morning, noon, evening) may be added to the classification method, and an example of classification based on outdoor brightness using the first example above. For example, it is classified as: Outdoors under 30 years old are bright, Outdoors below 30 years are dark, Outdoors between 30 and 50 years are bright, Outdoors between 30 and 50 years are dark, and so on. . These classification conditions are stored as data files in a data storage device such as an HDD (not shown) provided in the traveling characteristic data generation device 10.

  The travel history data 102 is data collected by the travel characteristic data generation device 10 via the data communication device 12 and the communication network 14 from the travel history data 118 for each vehicle measured and accumulated by the in-vehicle device 11. Similarly to the driver attribute classification condition 100, the data of the travel history data 102 is also stored as a data file in the data storage device. An example of the data format of the travel history data 102 is shown in FIG. A method for generating the travel history data 102 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. An example of the data format of the driver profile 106 is shown in FIG. The driver profile is composed of a driver ID (same as that included in the travel history data), a date of birth, a gender, a license acquisition date, and a cumulative driving distance, which are IDs for identifying individual drivers. By referring to these pieces of information, driver attribute classification can be performed. 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 based on the driver attribute classification condition 100 and the driver profile 106. Details of this classification processing will be described later together with the processing flow.

  The reference traffic information data 104 is reference traffic information data to be compared in order to determine what tendency the travel history data for each attribute classification classified by the data classification unit 101 has. 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 are used. The reference traffic information data 104 is stored as a data file in a data storage device such as an HDD in the travel characteristic data generation device 10. An example of the data format of the reference traffic information data 104 is shown in FIG. As shown in this figure, the reference traffic information data 104 includes a link number, a link length, and a reference travel time.

  The attribute classification-specific travel data generation unit 103 analyzes the trend of the travel history data with respect to the reference traffic information data by comparing the travel history data classified by the data classification unit 101 with the reference traffic information data 104, and the reference traffic information The attribute characteristic-specific traveling characteristic data 105 which is a parameter for correcting the data is generated.

  An example of the data format of the attribute classification-specific traveling characteristic data 105 is shown in FIG. The attribute characteristic-specific traveling characteristic data 105 includes correction parameters for each link for each of the attribute classifications 81 to 84 described above. The correction parameter is a value for correcting the reference traffic information data 104. In this embodiment, the travel time [second] for each link is used as the reference travel time which is the reference value in the reference traffic information data 104, and therefore the correction parameter is also the amount [second] for correcting the link travel time. . When the correction parameter is positive, correction is made to increase the reference travel time, and when the correction parameter is negative, correction is made to decrease the reference travel time. 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 travel characteristic data generation device 10 generates the attribute classification-specific travel characteristic data 105 that is a correction parameter for each attribute classification. As a result, even for a link for which no driving history data of the driver exists, driving characteristic data 105 by attribute classification is generated from the driving history data of another driver belonging to the same attribute classification as that driver, and the driving characteristic data by attribute classification is generated. By using 113 in the in-vehicle device 11 and correcting the reference traffic information data 112, it becomes possible to obtain highly accurate corrected traffic information.

  Next, the process of each part which implement | achieves operation | movement of the vehicle-mounted apparatus 11 shown in FIG. 2 is demonstrated.

  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 data 112 is equivalent to the reference traffic information data 104 in the driving characteristic data generation device 10, and the reference traffic information data 104 of the driving characteristic data generation device 10 is obtained via the data communication device 12 and the communication network 14. It is created by downloading or receiving data from the traffic information center via FM multiplex broadcasting, DSRC or beacon communication. 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 device 10, and the attribute classification by the traveling characteristic data generation device 10 via the data communication device 12 and the communication network 14. It is obtained by downloading the classification traveling characteristic data 105. Alternatively, the attribute characteristic-specific traveling characteristic data 105 held in the traveling characteristic data generation device 10 is temporarily stored in a storage medium such as a DVD-ROM, and is then read by a reading device (not shown) and copied or moved to the in-vehicle device 11. May be.

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

  The search data 115 stores data used for route search and guidance other than data defined in the format of the map data 119 shown in FIG. 3 for each mesh code representing the area identification ID. The search data 115 includes information on road structures other than roads included in the corresponding mesh area (for example, name, type, coordinate information, etc.), green zones and mountainous areas, rivers, lakes, seas, and the like. Information on water systems such as is also included.

  The route search unit 114 refers to the map data 119 and the search data 115 stored in the data storage device 42, and the current location obtained from the GPS receiver 48 or the departure point set by the user and the destination setting unit 110. A process for searching for an optimum route (recommended route) connecting to the destination set in step 1 is performed. In addition, the route guidance unit 116 refers to the map data 119 stored in the data storage device 42 and guides the route obtained by the route search unit 114 to the user using the voice input / output device 43 and the display 41. Perform guidance processing.

  The travel history accumulating unit 117 is measured by various data of the map data 119 stored in the data storage device 42, various sensors of the traveling wheel speed sensor 45, the geomagnetic sensor 46, the gyro sensor 47, and the GPS receiver 48. Based on the position information and data from outside the in-vehicle device 11 obtained via the in-vehicle LAN device 49, a travel history is generated, and similar to the travel history data 102 of the travel characteristic data generation device 10 as shown in FIG. According to the data format, a process of outputting as travel history data 118 is performed.

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

  The “driver ID” is obtained by referring to the driver ID number assigned to the in-vehicle device 11 and 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 / time” is information in units of one second obtained from the GPS receiver 48 of the in-vehicle device.
This represents the passage date and time of the link. As the link passage date and time, it is recorded uniformly as the passage date and time of either the link start node or the end node, but here, the date and time of passage through the link start node is recorded.

  The “link number” is the current position information obtained from the latitude / longitude information obtained at a frequency of 1 second or less from the wheel speed sensor 45, the geomagnetic sensor 46, the gyro sensor 47, and the GPS receiver 48 of the in-vehicle device. It is specified by matching each coordinate in the link including the start node and the end node of the link included in the map data 119 stored in the data storage device 42.

  The “link length” is included in the map data 119 and can be referred to 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 the 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 state” 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, and outdoor brightness is obtained from an illuminance sensor provided in the automobile.

  In this way, the travel history data 118 in which the travel history of the automobile 13 is accumulated in record units is collected in the travel characteristic data generation device 10 via the data communication device 12, and the travel history data 102 is generated.

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

  First, the data file of the driver attribute classification condition 100 is read. Further, the travel history data 102, the driver profile 106, and the reference traffic information data 104 are read (S51).

Next, the data classification unit 101 classifies the read travel history data 102 based on the read driver attribute classification condition 100 and the driver profile 106. Here, as the driver attribute classification condition, the above-mentioned younger age group younger than 30 years is 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 elderly 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 the current date and time of the drivers whose driver IDs are 1 and 2 (for example, From January 10, 2009), the driver's age is 33 years old and 30 years old, respectively, and since both are 30 years old and younger than 50 years old, they belong to attribute classification 2. Therefore, all the travel history data related to the drivers with driver IDs 1 and 2 are classified as belonging to attribute classification 2. Thus, all the records of the travel history data are classified based on the driver attribute classification condition 100 (S52).

  Next, the attribute-specific travel characteristic data generation unit 103 analyzes the tendency of the travel history data with respect to the reference traffic information data by comparing the classified travel history data 102 with the read reference traffic information data 104, A correction parameter for correcting the traffic information data is generated. The generated correction parameters are stored as attribute classification-specific traveling characteristic data 105 (S53).

Here, when the example of the travel history data classified by the data classification unit 101 is the value shown in FIG. 4 and the example of the reference traffic information data 104 is the value shown in FIG. Comparing the time with the reference travel time, it can be seen that the link travel time in the travel history data is shorter than the reference travel time in the reference traffic information data.
If this is converted into speed, it can be seen that the driving of each driver remaining in the travel history data tends to travel at a higher speed than the reference traffic information data.

  By such trend analysis, as shown in Equation 1 below, 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, and this difference (Th−Tb) Each average value Ta is used as a correction parameter for correcting the reference traffic information data.

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

  In this example, the correction parameter is obtained using the travel time of the link, such as the link travel time of the travel history data and the reference travel time of the reference traffic information data. When the reference value is used, 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, for each link, an area-related code (secondary mesh code) and a road type (highway, general road, etc.) are associated, and these are aggregated by referring to them. A correction parameter for each unit can be realized.

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

  First, in the destination setting unit 110, a destination is set by an HMI operation on the voice input / output device 43 or the input device 44 by the user (S61). In the destination setting process here, a method of inputting a cursor moved on the map display screen as a destination, or selecting from a destination (POI) list after selecting a genre in a destination search menu, or A method of setting a destination by a search specifying a telephone number, an address, etc. is taken.

  Next, the traffic information correction unit 111 reads the reference traffic information data 112 and the attribute characteristic-specific travel characteristic data 113 (S62). Then, the traffic information correction unit 111 corrects the traffic information of the reference traffic information data 112 using the read attribute characteristic-specific traveling characteristic data 113 (S63).

  A process for correcting the traffic information of the reference traffic information data 112 will be described. Assuming that the correction parameter for each attribute classification of the driver based on the driving characteristic data for each attribute classification is Ta and the traffic information based on the reference traffic information data is Tb, the corrected traffic information data Tb ′ is expressed as in the following (Equation 2). The

  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. By obtaining the correction parameter Ta in the travel characteristic data by attribute classification from the travel characteristic data generation device 10, the traffic information Tb based on the reference traffic information data held in the in-vehicle device 11 is corrected, and the corrected traffic information with high accuracy is obtained. Data Tb ′ can be obtained.

  Here, in this example, the correction parameter Ta in the attribute classification-specific traveling characteristic data is described as being generated for each link. However, as described above, the attribute classification-specific traveling characteristic data generated for each link is used as the correction parameter Ta. It is possible to aggregate in a predetermined unit such as area or road type, and use the average value as a correction parameter in the attribute characteristic traveling characteristic data for each aggregation unit. In that case, the correction parameter ( Traveling characteristic data) can be collectively applied to the reference traffic information data of the links corresponding to the respective aggregation units.

  Next, in the route search unit 114, as described above, it is obtained from the various data obtained from the wheel speed sensor 45, the geomagnetic sensor 46, the gyro sensor 47 of the in-vehicle device 11 and the position information measured by the GPS receiver 48. In addition, an optimum route (recommended route) is searched with reference to the search data 115 for a route connecting the current location or the departure point set by the user and the destination set by the destination setting unit 110 (S64). At this time, by using the corrected traffic information data Tb ′, the shortest time route when the travel time is set as the link cost is calculated as a recommended route by the minimum cost route search method called the Dijkstra method.

  Next, the route guidance unit 116 creates guidance data necessary for guidance from the map data stored in the data storage device 42 based on the information of the searched recommended routes. 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, In addition, information on the distance to the guidance intersection is included. Then, based on the created guidance data, route guidance is performed according to the current location obtained sequentially (S65).

  For example, the distance from the current location to the nearest guidance intersection is displayed on the display 41 or notified by voice. In addition, if there is a guided intersection within a predetermined distance from the current location, the distance to the guided intersection displayed on the display 41 will be updated sequentially, and a voice will be heard as “the intersection is left in the direction of 500 meters ahead”. Guide in the direction. Further, the total travel time to the destination is calculated by adding the travel time of each link constituting the calculated recommended route. Then, the estimated arrival time at the destination is calculated from the current time and the total travel time. Here, there are two types of travel time for each link, that is, before the correction, that is, the travel time of the reference traffic information and the corrected travel time. However, when the travel time after correction does not exist, the travel time before correction is substituted. Therefore, a value based on two types of travel times before and after the correction is calculated for the estimated arrival time at the destination.

  FIG. 10 shows a display example of the display 41 at the time of route guidance in 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 an estimated time of arrival at the destination, of which “standard” is a value before correction, that is, a value calculated based on reference traffic information, and “correction” is traffic corrected by the travel characteristic data by attribute classification. It is a value calculated based on information. Thereby, the user can know what the correction result by the attribute classification of the driver is compared with the reference value.

  Next, as described above, the travel history accumulating unit 117 measures the various data such as the map data stored in the data storage device 42 and the various sensors such as the wheel speed sensor 45, the geomagnetic sensor 46, and the gyro sensor 47. The travel history data 118 is generated and stored by measuring and processing the travel data of the vehicle based on the measured value and the positioning result measured by the GPS receiver 48 (S66). In addition, since the driving data of the vehicle can be measured and processed after the engine of the automobile 13 is started and the in-vehicle device 11 is started, the processing of the traveling history accumulation unit 117 is related to the processing from S61 to S65. It is executed regularly.

  As a result, in the in-vehicle device 11, even when there is no driving history data of the driver, the driving characteristic data generating device 105 generates the driving characteristic data 105 by attribute classification generated from the driving history data of another driver belonging to the same attribute classification as the driver. 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, even when the driving history data of the driver is not sufficiently present, the driving characteristic data by attribute classification based on the driving history data of another driver belonging to the same attribute classification as the driver is applied. The accuracy of the traffic information has been improved by correcting the reference traffic information data 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, such as after repeated use, it is considered that the accuracy is higher when the reference traffic 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 travel characteristic data generation device 10 in the second embodiment of the present invention is the same as that in the first embodiment.

  However, unlike the first embodiment, the in-vehicle device 1011 is 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 as shown in the functional block diagram of FIG. It has a function of generating driving characteristic data of the driver. 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, the process of each part which comprises the vehicle-mounted apparatus 1011 of FIG. 11 is demonstrated. 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 related to individual drivers, similarly to the driver profile 106 in the driving characteristic data generation apparatus 10 of the first embodiment. However, in the second embodiment, it is sufficient that the driver can be identified. 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 obtaining the driving history data. The tendency with respect to the reference traffic information data 112 is analyzed, and the attribute characteristic-specific traveling characteristic data 120 which is a parameter for correcting the reference traffic information data 112 is generated and output. The data format of the attribute classification-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 data classification unit 122 selects a driver to be processed from the driver profile 121 (S71). In selecting, the user may select from the driver list displayed on the display 41 of the in-vehicle device 1011 by the HMI, or the driver IDs are automatically selected in order from the lowest number registered in the driver profile 121. May be. 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, various data necessary for processing such as reference traffic information data 112, travel history data 118, and travel characteristics data 120 by attribute classification are read (S72).

  Next, the driver ID of the user profile 121 is specified for the driver selected in the process of S71, and the travel history data 118 is searched to extract a data record corresponding to the specified driver ID (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 amount of travel history data extracted in the process of S73 is analyzed. Here, first, the number of records of the extracted travel history data is obtained. Here, as an example of a method for determining the data amount, a method using two threshold values (ε1, ε2) is shown. Here, it is assumed that ε1 and ε2 are both positive numbers and have a relationship of ε1> ε2. If the number of extracted records is larger than ε1, it is determined that there is a sufficient amount of travel history data for the selected driver, and 1 is given as the data amount determination value. If the number of extracted records is equal to or less than ε1 and larger than ε2, it is determined that there is a minimum amount of travel history data for the selected driver, but 2 is given as the data amount determination value. If the number of extracted records is equal to or less than ε2, it is determined that there is no minimum amount of travel history data for the selected driver, and 3 is given as the data amount determination value (S73).

  Here, the method of determining by the total number of records is exemplified, but as another method, the determination can also be made based on the result of further classification with values corresponding to items included in the extracted records. For example, in the case of the data format of the travel history data shown in FIG. That is, consider the case where the “road surface state” is divided into “dry”, “wet”, and “freeze” as described above. Also, consider the case where the “outdoor brightness” is divided into “bright” and “dark”. Here, in an example where all records are separated by the value of “road surface condition”, the total number of records should be divided into “dry” record numbers, “wet” record numbers, and “freeze” record numbers. The above-described data amount determination may be performed for each number of records. In this case, depending on the travel history data, the same data amount determination value may be used for all road surface conditions, or different data amount determination values may be used. The same applies to the case where all records are separated by the value of “outdoor brightness”.

  In the process of S74, when 1 or 2 is given as the data amount determination value, the process proceeds to S7 because the travel history data amount is sufficient, and when the data quantity determination value is 3, the travel history data amount is It is determined that it 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 process proceeds to the driving characteristic data generation unit 123 for each driver. In the process of S74, when 1 is given as the data amount judgment value, the driving characteristic data generation method 1 for each driver is selected, and when 2 is given as the data quantity judgment value, the driving characteristic data generation method 2 by driver is selected. The driving characteristic data generation method for each driver to be executed in S76 is determined (S75).

  Next, in accordance with the driving characteristic data generation method for each driver determined in S75, a correction parameter for the corresponding driver is generated and added to the attribute characteristic-specific driving characteristic data 120 (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 selected driver, the driving characteristic of the driver is sufficiently reflected. That is, assuming that the link travel time in the extracted driving history data of the driver is Th and the reference travel time of the reference traffic information data 112 is Tb, the correction parameter Ta expressed by the above-described (Equation 1) is obtained. This Ta is an average value of the difference between the driving history data of the selected individual driver and the reference traffic information data, and represents a driving characteristic of the driver.

In the driving characteristic data generation method 2 by driver, the driving characteristic of the driver cannot be sufficiently reflected because the selected driver is not sufficient but there is a minimum amount of driving history data. The driving characteristic data of the driver attribute with the closest driving characteristic is selected. Specifically, first, assuming that the link travel time in the extracted driving history data of the driver is Th, and the reference travel time in the reference traffic information data is Tb, the correction parameter Ta expressed by the above (Formula 1) is temporarily set. It is obtained for each link, and this is the driving characteristic of the driver. Next, reference is made to the travel characteristic data Ta ′ _j for each attribute classification already stored in the travel characteristic data 120 for each attribute classification. 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 by the following (Equation 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 degree of divergence is obtained for all 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, the correction parameter (travel characteristic data) obtained in each aggregation unit can be collectively applied to the traffic information of links belonging to the aggregation unit.

  Next, it is determined whether all users registered in the driver profile 121 have been processed (S77). If all the users have not been processed, the process returns to 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 driving characteristic data for each driver and can update the driving characteristic data 120 for each attribute classification. 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 traffic information of the reference traffic information data 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 data 103 Attribute classification-specific travel characteristic data generation unit 104, 112 Reference traffic information data 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 (9)

A vehicle having a storage means for storing traffic information data and map data used for route search, and a route search means for searching for a recommended route from the departure point to the destination based on the traffic information data and map data. In the route guidance device,
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;
A traffic information correcting means for correcting the traffic information data using attribute classification-specific traveling characteristic data of an attribute classification to which the driver of the vehicle corresponds;
With
The route guidance device, wherein the route search means searches for a recommended route using the traffic information data corrected by the traffic information correction means. 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 each attribute classification using the driving history data and the traffic information data classified for each driver registered in the driver profile; Prepared,
2. The route guidance device according to claim 1, wherein the traffic information correction unit corrects the traffic information data using the attribute classification-specific travel characteristic data or the driver-specific travel characteristic data. 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. The generation according to claim 2, wherein the generation of the driving characteristic data for each driver is generated based on the driving characteristic data for each attribute classification when the generation number is less than the first predetermined number and greater than the second predetermined number. The route guidance apparatus described. 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 in-vehicle device according to claim 3, wherein the driving characteristic data generating unit determines a method of generating the driving characteristic data for each driver based on an amount of driving history data for each classified user ID. 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. The in-vehicle device according to claim 4, wherein the on-vehicle device is obtained. 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 driving characteristic data generating means for generating attribute-specific driving characteristic data for correcting the traffic information data for each attribute class using the driving history data classified for each attribute. Characteristic data generator. 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,
The travel characteristic data generating apparatus according to claim 6, wherein the attribute classification is determined based on information included in the driver profile and the travel history data. 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 travel characteristic data generation apparatus according to claim 6, wherein the traffic information data of each link is obtained as attribute characteristic-specific travel characteristic data for correcting for each attribute classification. The route guidance device according to claim 1,
The driving characteristic data generation device is a driving characteristic data generation device according to claims 6 to 8,
An in-vehicle information system comprising a data communication device and a communication network connecting the travel characteristic data generation device and the route guidance device.

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