JP2009031046A - Fuel consumption estimation system of automobile, route searching system, and driving guiding system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To estimate the tendency of fuel consumption of each driver highly accurately in consideration of individuality of the driver or the state of an automobile or a road, whereas the fuel consumption is changed according to the operation state of each driver such as the degree of economical driving, and thereby sweeping prediction thereof is difficult. <P>SOLUTION: This fuel consumption estimation system has a fuel cost information processing operation part 903 for acquiring information necessary for determining fuel cost following a driving history from a vehicle, and determining a fuel cost tendency characteristic of a driver individual of the vehicle based on the information; a storage part 224 for storing the fuel cost tendency characteristic; economical driving degree setting parts 900, 903 for inputting the degree of economical driving aimed by the driver or setting it through an estimation means; and a predicted fuel consumption operation part 910 for predicting the fuel consumption along a traveling scheduled route from at least the set degree of economical driving and the fuel cost tendency characteristic of the driver individual. The fuel cost tendency characteristic may be managed in each pattern of the driving state including at least one of vehicle state information and state information of a traveling route. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to an automobile fuel consumption estimation system, and further to an optimum route search and a driving guidance system in consideration of a driving tendency of a driver by using the fuel consumption estimation system.

  As a conventional method for estimating fuel consumption of an automobile, there is a method of collecting data actually traveled for each link of a route and estimating based on the data, as shown in Patent Document 1.

  Moreover, as shown in Patent Document 2, there is a method of creating a fuel consumption correspondence table from road gradient, speed, and traffic jam information, and obtaining a fuel amount for each link of a route.

  Furthermore, as shown in Patent Document 3, there is a method of correcting the fuel consumption amount using information on the degree of acceleration for each driver.

Patent 2759815 JP 2006-98174 A JP 2006-300780 A

  In the above-described prior art, the fuel consumption can be obtained on an average for each link, or can be obtained by performing correction based on an average acceleration for each driver. However, the actual fuel consumption is the driving situation when the driver is in a hurry, wants to enjoy a sense of acceleration, etc., or if he wants to eco-drive (driving with high fuel efficiency) because he has enough time. There is a tendency to change greatly depending on. In addition, the degree of eco-driving and accelerator operation varies depending on the driver, and the minimum fuel consumption and the maximum fuel consumption cannot be determined unconditionally.

  The problem to be solved by the present invention is to propose a system capable of predicting the fuel consumption amount for each driver in consideration of the individuality of the driver when estimating the fuel consumption amount of the planned travel route. Another object of the present invention is to propose a system for estimating fuel consumption with high accuracy in consideration of the situation of automobiles and roads.

  In order to solve the above problems, the present invention proposes a system having the following basic configuration.

  (1) First, in a fuel consumption estimation system for an automobile, information on at least fuel consumption and travel distance associated with the driving history is acquired from the vehicle, and the individual fuel consumption tendency of the driver of the vehicle is updated based on the information. The fuel consumption amount of the planned travel route is predicted based on this fuel consumption tendency.

  In this case, in addition to acquiring information on the fuel consumption and travel distance associated with the driving history from the vehicle, information on driving status including at least one of vehicle state information and driving path status information associated with the driving history is acquired. Then, it is possible to update the fuel consumption tendency for each driving situation based on the information, and manage this fuel consumption tendency for each driving situation pattern. When the fuel consumption amount of the planned travel route is predicted, a driving situation pattern having a high correlation with the route information of the link is searched for each link of the planned travel route, and the fuel consumption tendency of the driving situation pattern is searched. Based on this, the fuel consumption amount of the planned travel route may be predicted.

  The vehicle state is, for example, at least one of the weight of the vehicle, the lighting state of the light, the operating state of the wiper, the operating state of the air conditioner, the temperature, and the driver ID. The route status information is at least one of a road type such as an expressway and a general road, speed limit, gradient information, curve curvature information, and the number of traffic lights.

  (2) The other is, for example, in a fuel consumption estimation system for automobiles, which acquires information necessary for obtaining the fuel consumption associated with the driving history from the vehicle, and based on the information, the individual driver's fuel consumption tendency A fuel consumption information processing calculation unit for obtaining characteristics, a storage unit for storing the fuel consumption tendency characteristics, an economic driving degree setting unit for setting a driver's target economic driving degree through input or an estimation means, and at least the setting And a predicted fuel consumption calculation unit for predicting the fuel consumption of the planned travel route from the measured economic driving degree and the fuel consumption tendency characteristics of the individual driver. As a method for obtaining the fuel consumption tendency characteristic, for example, the following is proposed.

  One is to create a fuel frequency distribution for each individual driver based on at least the travel distance and fuel consumption information associated with the driving history acquired from the vehicle. Get trending characteristics. This embodiment will be described later with reference to the embodiment of FIG.

  The other is to accumulate the fuel consumption of the fuel consumption frequency distribution, and obtain the fuel consumption tendency characteristic in association with the economic driving degree.

  When the economic driving degree is set through the estimation means, the driver's economic driving degree can be estimated from the fuel consumption tendency associated with the latest driving history, or the driver can set it through an interactive screen.

  Furthermore, the following is proposed as an application system of the fuel consumption estimation system according to the present invention.

  (3) First, using the fuel consumption estimation system, calculating the amount of fuel consumed for a plurality of scheduled travel routes from the current location to the destination, and the fuel for the determined planned travel routes The present invention proposes a route search system including a step of displaying a route in which fuel consumption is minimized or a route in which fuel consumption is ranked in terms of consumption.

  Further, in the route search system, the means for inputting the fuel unit price, the means for inputting the unit price for estimating the cost per hour of the driver, and the fuel consumption obtained by the fuel consumption estimation system are input. A means for converting the amount of money based on the unit price of fuel, a step of obtaining travel time for each route based on a driver's fuel consumption tendency and traffic congestion, a step of converting the amount of travel time obtained using the input unit price of time, and a route A route search system having a step of obtaining a toll for a toll road in the middle and a step of summing values of amounts necessary to pass each route and displaying a route with a minimum amount or a ranked route suggest.

  (4) Further, a driving guidance system is provided that includes means for dividing the fuel consumption distribution information for each set period and displaying the fuel consumption distribution information in time series using the fuel consumption estimation system.

  Furthermore, using the fuel consumption estimation system, means for collecting fuel consumption distribution information accumulated for each vehicle from a plurality of vehicles, fuel consumption distribution information collected from the plurality of vehicles, and fuel consumption distribution information of a specific vehicle In comparison, a driving guidance system having means for displaying a difference in distribution between the specific vehicle and other vehicles is proposed.

  According to the fuel consumption estimation system according to the present invention, it is possible to capture the driver's individual fuel consumption tendency associated with the driving history, and to improve the prediction accuracy of the fuel consumption of the planned travel route. Further, when a fuel consumption tendency corresponding to the past vehicle situation and route situation is acquired, it is possible to further estimate the fuel consumption amount reflecting the driver's situation.

  In addition, according to the route search system of the present invention, it is possible to perform energy saving route and cost estimation and route selection in accordance with the driver's awareness.

  Moreover, according to the driving guidance system of the present invention, it is possible to present how the driving tendency is changing.

  First, the fuel consumption used in the embodiment of the present invention is defined. Normally, the travelable distance per unit fuel consumption is expressed as fuel consumption. In the following embodiments, the fuel consumption per unit travel distance, which is the inverse of the distance, is referred to as fuel consumption.

  FIG. 1 is a schematic configuration diagram of a navigation system to which an embodiment of the present invention is applied. The navigation system according to the present embodiment collects the car navigation device 101 mounted on the vehicle, the information providing center 106 that provides the car navigation device 101 with various data such as point information and traffic information, and the nationwide traffic information. The traffic information center 107 provides traffic information to the information providing center 106 connected by the dedicated line 108.

  In this example, the car navigation apparatus 101 captures information (vehicle information) related to the driving situation of the vehicle on which the car navigation device 101 is mounted and information necessary for fuel consumption information processing, and also captures traffic information from the information providing center 106. Based on the driving situation pattern, the driver's individual fuel consumption tendency (in the embodiment, the fuel consumption frequency distribution and its cumulative total) is calculated. Furthermore, the car navigation apparatus 101 calculates the predicted fuel consumption of the planned travel route from the fuel consumption tendency and the economic driving degree set in advance through input or estimation means. Such fuel consumption information is sent to the information providing center 106. The information providing center 106 receives fuel efficiency information from a large number of vehicles, stores the fuel efficiency information, analyzes the fuel efficiency information (for example, comparative analysis of the fuel efficiency information between the vehicles), and analyzes each result. It is set to be provided to a car navigation device of a vehicle. The details will be described below.

  The car navigation device 101 is connected to a communication device such as a mobile phone 102, and communicates with the information providing center 106 from the mobile phone 102 via the wireless base station 104 and the network 105. In addition, the car navigation apparatus 101 is configured to be able to connect various sensors mounted on a vehicle such as a GPS receiver 103 and a vehicle speed sensor and a gyro sensor (not shown). Although one car navigation apparatus 101 in FIG. 1 is illustrated as an example, in reality, a large number of car navigation apparatuses are targeted for receiving services from the information providing center 106. The traffic information center 107 can be realized by a known computer system.

  FIG. 2 is a schematic configuration diagram of the information providing center 106. The information providing center 106 includes a CPU 401, a memory 402, an external storage device 403 such as a hard disk device, a reading device 409 that reads data from a portable storage medium 410 such as a CD-ROM or a DVD-ROM, a keyboard, An input device 407 such as a mouse, an output device 408 such as a monitor, a communication device (1) 405 that communicates with the traffic information center 107, a communication device (2) 406 that communicates via the network 105, and each of these devices And a bus 404 for connecting the two. The information providing center 106 may include a plurality of computer systems having such a configuration. Each computer system can be constructed on a distributed network system configured by connecting each computer system using a network interface (not shown).

  FIG. 3 is a functional block diagram of the information providing center 106. The information providing center 106 includes a communication device (1) 405, a communication device (2) 406, a request receiving unit (interface) 302, an information providing unit 303, a traffic information processing unit 307, a traffic information acquisition unit 308, and a fuel consumption information analysis unit 320. , A map information DB (Data Base) 310, a fuel efficiency analysis result DB 311, a predicted traffic information DB 313, a traffic information DB 312 and a fuel efficiency information DB 321. The information providing unit 303, the traffic information processing unit 307, the traffic information acquisition unit 308, and the fuel efficiency information analysis unit 320 are configured by the CPU 401. The map information DB 310, the fuel efficiency analysis result DB 311, the predicted traffic information DB 313, the traffic information DB 312, and the fuel efficiency information DB 321 are held in the external storage device 403.

  The communication device (2) 406 communicates with the car navigation device 101 connected via the network 105. The request receiving unit 302 receives a request from the car navigation device 101 connected via the communication device (2) 406 and transmits the request to the information providing unit 303.

  The information providing unit 303 provides information corresponding to the request sent from the car navigation device 101 via communication to the car navigation device 101 via the communication device (2) 406. The information providing unit 303 includes a map providing unit 304, a fuel consumption analysis result providing unit 305, and a traffic information providing unit 306.

  When map data is requested from the car navigation apparatus 101, the map providing unit 304 calls map data of a predetermined area including the position of the point designated at the time of request from the map information DB 310. The called map data is stored as a file and then provided to the car navigation apparatus 101. Note that the map information stored in the map information DB 310 can have the same configuration as the map information 220 held by the car navigation device 101 described later.

  When traffic information is requested from the car navigation device 101, the traffic information providing unit 306 accesses the predicted traffic information DB 313 or the traffic information DB 312 via the traffic information processing unit 307 and searches for the requested information. The searched information is temporarily stored as a file and then provided to the car navigation apparatus 101.

  The fuel consumption information analysis unit 320 collects fuel consumption information calculated by the car navigation device 101 of each vehicle to be serviced (the calculation of this fuel consumption information will be described later) and stores it in the fuel consumption information DB 321. The fuel consumption information calculated by the car navigation device 101 is information related to the fuel consumption tendency of individual vehicles, in other words, individual drivers. The fuel consumption information analysis unit 320 analyzes the collected fuel consumption information (fuel consumption trend information, fuel consumption frequency distribution information) from a plurality of vehicles in accordance with an instruction from the vehicle (navigation device 101) via the request reception unit 302 (for example, the own vehicle). And the result is stored in the fuel efficiency analysis result DB 311. Further, when a request for the analysis result is received from the vehicle side via the request receiving unit 302, it is provided to the requesting vehicle (car navigation device 101) via the fuel consumption analysis result providing unit 305. In addition, it is also possible to use a general-purpose personal computer as the request source and the destination of the information of the analysis result via the Internet.

  FIG. 4 shows a configuration example of traffic information provided (downloaded) to the car navigation device 101 by the traffic information providing unit 306. The traffic information to be downloaded includes traffic jam / travel time information 1030, accident / regulation information 1040, and the like.

  The traffic jam / travel time information 1030 is information used for route search. The traffic jam / travel time information 1030 includes, for each link ID 1031 of each link, a representative traffic jam degree 1032, a link travel time 1033, the number of sections 1034, and section information 1035. Here, the link refers to a road connecting intersections.

  The link ID 1031 is a unique number for uniquely identifying the target link, and corresponds to the map information link ID in the map information DB 310. The representative traffic congestion degree 1032 is traffic congestion information indicating the traffic congestion status of the entire target link, and is indicated by three levels of traffic congestion, congestion, and smoothness. The link travel time 1033 is information indicating the time required to travel from the end to the start of the target link. The number of sections 1034 is the number of sections further divided within the link. The method of setting the section is arbitrary, but for example, it is set by the installation position interval of the traffic monitoring monitor on the road. The section information 1035 is information indicating a detailed traffic jam situation for each section in the target link. The section information 1035 includes a section head position 1037 for specifying a section, a section length 1038, and a congestion degree 1036 in the section.

  FIG. 5A is a diagram illustrating a specific configuration example of the traffic jam / travel time information 1030. FIG. 5B is a diagram showing the state of the link in the example. That is, when the traffic jam / travel time information 1030 of the link whose link ID 1031 is “1” is configured as shown in FIG. 5A, it can be seen from the number of sections 1034 that this link consists of three sections. Based on the information on the congestion degree 1036, the head position 1037, and the section length 1038, the section 1 is a smooth section of 200 m, the section 2 is a congested section of 110 m, and the section 3 is a congested section of 190 m. I understand. In the example of FIG. 5, the number of sections is set to 3, but it can be expressed by an arbitrary number of sections according to the traffic congestion in the link.

  The accident / regulation information 1040 includes point coordinates 1042, a link ID 1043, and an accident / regulation type 1044.

Returning to FIG. The traffic information acquisition unit 308 periodically downloads the latest traffic information from the traffic information center 107 connected by the dedicated line 108 and the communication device (1) 405, and stores it in the traffic information DB 312. The traffic information processing unit 307 uses the traffic information accumulated in the traffic information DB 312 to predict a traffic situation several minutes, hours, or days ahead, and stores the predicted information in the predicted traffic information DB 313. The traffic information processing unit 307 predicts near-future traffic conditions using, for example, the following method.
(1) The traffic information stored in the traffic information DB 312 is classified into day types such as weekdays and holidays, and the average value of travel time data or traffic jam data in the same time zone is obtained for each category, and the value is determined as the target time zone. To apply to the predicted value of.
(2) A time series data extrapolation method that obtains a curve (straight line) to be extrapolated from the current time series data by a method such as autoregression, Kalman filter, neural network, etc. and predicts the traffic situation in the near future.
(3) A method of making predictions using data of similar days by comparing the time series data of the past day and the time series data of the current day.

  The traffic information processing unit 307 generates predicted traffic information by predicting a traffic situation several minutes, hours, or days ahead using any of the above methods, and stores the predicted traffic information in the predicted traffic information DB 313.

  FIG. 6 is a schematic configuration diagram of the car navigation apparatus 101 mounted on the vehicle. The car navigation apparatus 101 connects the CPU 201, the RAM 202 that functions as a work area of the CPU 201, the flash ROM 203 that stores various programs for realizing each function that can be provided by the car navigation apparatus 101, and the mobile phone 102. Mobile phone IF (interface) 204 for storing, a storage device 210 such as an HDD (hard disk) for storing various information, and a sensor IF 206 for connecting various sensors such as a GPS receiver 103, a vehicle speed sensor, and a gyro sensor. An operation button group 207, a remote control light receiving unit 205 that receives a signal from the remote control 110, a display 208, and a bus 209 for connecting them together.

  Information stored in the storage device 210 includes map information 220, traffic jam level information 222, fuel consumption information 224, traffic information 226, and route information 228.

  The map information 220 stores node data related to intersections and link data related to roads connecting the intersections for each mesh divided into secondary mesh sections. The secondary mesh section is an area approximately 10km square, divided into 12 divisions per north latitude and 8 divisions per east longitude. FIG. 7A is a diagram illustrating a configuration example of the node data 2201 stored for each secondary mesh section. The node data includes, for each node record, a node number 2202 that can uniquely identify the node, a latitude and longitude 2203, and a connection link number 2204 related to a link that is connected to the node. FIG. 7B is a diagram illustrating a configuration example of link data. The link data includes, for each link record 2211, a link number 2212 that can uniquely identify the link, a link length 2213, a road type 2214 that can determine whether the link is a general road or a highway, a lane The number 2215, the regulation speed 2216, the connection node number 2217, the shape 2218, and the gradient 2219 indicating the slope of the road are included.

  The map information 220 may be stored in advance in the storage device 210 of the car navigation device 101, or may be downloaded from the information providing center 106 and used.

  The traffic congestion level information 222 is basic data used for calculating a link cost when searching for a route. The traffic level information 222 is stored separately for general roads, highways in cities, and highways outside cities. As shown in FIG. 8, the traffic congestion level information 222 includes information related to the traffic congestion level C (V) 2221 per unit length for the travelable speed V [km / h]. The traffic congestion level C (V) 2221 per unit length basically becomes smaller as the section can travel faster. The congestion level C (V) 2221 per unit length is divided into, for example, five levels. A section in which the travelable speed V is 10 km / h or less is set to 5 at the maximum level, and a section in which the travelable speed V is 40 km / h or more is set to 1 in the minimum level. In addition, this traffic congestion level does not need to be five steps, and each level value may not be a value limited to the range of 0-5.

  The traffic congestion level information 222 may be stored in advance in the storage device 210 of the car navigation device 101, or may be downloaded from the information providing center 106 and used.

  The fuel consumption information 224 is basic data for calculating a predicted fuel consumption amount to be described later.

  The traffic information 226 is information downloaded from the information providing center 106 and includes current traffic information.

  The route information 228 is information regarding the searched route. The route information 228 includes link information, node information, and the like constituting the route.

  FIG. 9 is a functional block diagram of the car navigation device 101. The car navigation apparatus 101 calculates the fuel consumption information for each driving situation, the dialogue processing unit 900 that performs information display to the user and dialogue processing, the storage unit 605 that stores information for route calculation and predicted fuel consumption calculation The fuel consumption information processing unit 903, the route search unit 907 for searching for a route to the destination based on information from the dialogue processing unit 900, and the fuel consumption necessary for traveling the candidate planned travel route The estimated fuel consumption calculating part 910 to estimate.

  The dialogue processing unit 900 includes the operation button group 207, the remote control 110, the remote control light receiving unit 205, the display 208, and the like shown in FIG.

  The storage unit 605 includes the RAM 202, the flash ROM 203, the storage device 210, and the like illustrated in FIG. 6, and stores data according to instructions from each processing unit.

  The traffic information acquisition unit 911 downloads traffic information including the current traffic situation from the information providing center 106 via the mobile phone 102 or the like periodically or in response to a request from the user. The downloaded traffic information is stored in the storage unit 605. Further, fuel consumption information is transmitted to the information providing center 106 as necessary.

  A fuel consumption information processing calculation unit 903 reads vehicle information 902, map information 220, traffic congestion level information 222, traffic information 226, and route information 228 from various sensors 901 of the vehicle, calculates the fuel consumption of the current driving situation, The fuel consumption information 224 is stored for each driving situation pattern. Details of the fuel consumption information calculation will be described later.

  The route search unit 907 includes the traffic information 226 received by the traffic information acquisition unit 911, the traffic congestion level information 222, the map information 220, and the destination information and search condition information 905 input by the user via the dialog processing unit 900. The link cost of each link is obtained based on the fuel consumption information 909 obtained by the predicted fuel consumption calculation unit 910 using these pieces of information, and the cost from the vehicle position to the destination is minimized using the Dijkstra method. Search for a route. The calculation of the predicted fuel consumption will be described later. Further, the searched route is displayed on the dialogue processing unit 900.

  The predicted fuel consumption calculation unit 910 includes fuel efficiency information 224, route driving status information 908 input from the route search unit 907, and economic driving degree indicating driver's driving awareness (hereinafter referred to as eco driving degree). Based on 904, the predicted fuel consumption 909 for the planned travel route is calculated and returned to the route search unit 907. The eco-driving degree is a degree from 0 to 10, for example, indicating the feeling of eco-driving about how much eco-driving the driver performs when traveling to the destination.

  Hereinafter, the operation of each processing unit will be described.

  The processing of the fuel consumption information processing calculation unit 903 is shown in FIG. The fuel consumption information processing calculation unit 903 starts processing when the vehicle is started, always captures the driving situation information accompanying the driving history and information necessary for the fuel consumption calculation, calculates the fuel consumption in association with the driving situation from these information, and the driving situation The fuel consumption information for each pattern (here, the fuel consumption frequency distribution indicating the fuel consumption tendency) is obtained. The series of processing steps is shown below.

  First, in step 2001, driving state information and fuel consumption information are acquired for a predetermined unit section such as 1 km traveling. The driving status information indicates vehicle information 902 from the vehicle sensor 901, map information 220, and traffic level information 222.

  Vehicle information 902 includes vehicle position, speed, fuel injection amount, engine speed, accelerator opening, wiper switch, air conditioner switch, light switch, temperature, weight, driver ID information indicating who is driving, etc. The values from the various sensors, the values obtained from the communication line (hereinafter referred to as CAN) connecting the vehicle control unit, and the information estimated from these values are shown. For example, when there is no sensor, the vehicle weight can be estimated from the relationship between engine output torque, acceleration, and road gradient. The driver ID may be input via the dialogue processing unit 900. However, a method of acquiring the driver ID from an ID assigned to the ignition key for each individual or identifying and acquiring the ID by voice recognition may be considered. Vehicle information 902 acquired from CAN or the like is input as n-dimensional time-series data (n is the number of acquired data). FIG. 11 is an example of time-series data of the vehicle information 902, and FIG. 12 is an example of time-series data represented as a time-series graph. Of the time series data, the fuel consumption amount information can be acquired from the integration of the fuel injection amount (“fuel” shown in FIG. 11) associated with the operation history. Moreover, it is possible to acquire a travel distance from the relationship between time information and speed. The fuel consumption may be input from a fuel meter, and the travel distance may be input from a travel distance meter. From the fuel consumption amount and the travel distance, fuel consumption information for a unit distance is obtained.

  On the other hand, data input as driving status information from the map information 220 includes road gradient, road type such as an expressway and a general road, speed limit, curve curvature distribution, the number of traffic lights, and the like. The road gradient information may use altitude difference as input data.

  In step 2002, it is determined whether or not the vehicle has traveled in the unit section described above. If the vehicle has traveled in the unit section, step 2003 and subsequent steps are performed.

  In step 2003, a highly correlated pattern is obtained from the past driving situation patterns already stored in the fuel consumption information 224 for the driving situation information of the unit section acquired in step 2001.

  In step 2004, based on the fuel information of the unit section acquired in step 2001, the fuel efficiency frequency corresponding to the highly correlated driving situation pattern obtained in step 2003 among the fuel efficiency frequency distribution data already stored in the fuel efficiency information 224. Update distribution data.

  The driving situation patterns in steps 2003 and 2004 are obtained on an n-dimensional space using the above-described driving situation information, that is, the vehicle information 902, the map information 220, and the traffic congestion level information 222 as axes. Here, FIG. 13 shows an example in which the average speed and gradient information are acquired as the driving situation information and simplified in two dimensions. In this example, the average speed and the gradient information are divided for each unit section in the two-dimensional space, and the state within each division is defined as one driving situation pattern. Each driving situation pattern holds a frequency distribution of fuel consumption. When the average speed and gradient of the unit section are, for example, a [km / h] and b [m / m], respectively, the range 2301 in the two-dimensional space is determined thereby. The driving situation pattern corresponding to this range 2301 is obtained, and the fuel economy frequency distribution 2302 corresponding to this driving situation pattern is updated based on the fuel consumption information of the unit section.

In the case of n-dimensionalization, there are problems in increasing the amount of data and detecting the similarity of correlation patterns, but this can be dealt with by the following method.
(1) Data groups that form similar patterns for clustering n-dimensional data space data are collectively handled as a group. As for the clustering method, a general method can be adopted, and detailed description thereof is omitted here.
(2) Setting a characteristic pattern in advance When the vehicle travels, if a combination of influential parameter ranges is known in advance, it is grouped in advance.
(3) Treat the relationship between n-dimensional space parameters and fuel consumption information in the feature space
The relationship between the n-dimensional parameter and fuel consumption information is obtained by principal component analysis, and the frequency distribution of fuel consumption is obtained by the combination of the obtained bases.

  As described above, the n-dimensional attribute data for obtaining the driving situation pattern can be selected from the vehicle information 902, the map information 220, and the traffic congestion level information 222. Specifically, it is information about a road (link) that is being traveled, which is determined from the position of the vehicle. Information is targeted. In addition, any one or a plurality of pieces of information among the time zone during travel, the weather, the temperature, the weight of the vehicle, the average speed, the degree of traffic jam, and the driver ID indicating who is driving are also targeted. In step 2001, the fuel consumption information processing calculation unit 903 collects these values together with the fuel consumption information for a certain period during traveling.

  In the method (1) based on clustering, the collected information is collected until the number of clusters (the number of patterns) set in advance is similar, and the frequency distribution of fuel consumption is obtained from the collected data. . A known measure such as Mahalanobis distance can be used as a measure of the distance indicating the similarity between data and between clusters.

  In the above example (2), data is classified in advance into characteristic attribute patterns. Specifically, it is possible to prepare multiple combinations that affect fuel consumption, such as a pattern such as daytime, high-speed driving, no traffic jam, and vehicle weight standard, and a pattern such as night, traffic jam, low speed, and additional vehicle weight. Conceivable. The actual fuel consumption information is collected for each pattern, and the frequency distribution is obtained.

  In the example (3) above, the correlation of the collected attribute data with the fuel efficiency information is obtained by principal component analysis. Expected fuel consumption information is obtained by the combination of the acquired main components with respect to the current situation. It is also possible to estimate fuel consumption information by multiple regression analysis using attribute data as explanatory variables.

  Step 2005 is processing when the eco-driving degree 904 is automatically acquired by the estimating means. In this case, first, a fuel consumption frequency distribution in a pattern having a high correlation with the current driving situation pattern is obtained from past fuel consumption frequency distribution data already stored in the fuel consumption information 224. In the calculated fuel consumption frequency distribution, the minimum fuel consumption value is 0 for eco-driving, the maximum fuel economy value is 10 for eco-driving, and the most recent fuel consumption is the minimum fuel economy value (eco-driving degree 0) to the maximum fuel economy value (eco-driving degree). 10), the eco-driving degree corresponding to the position is automatically acquired and stored in the eco-driving degree 904. Here, the latest fuel consumption is, for example, the fuel consumption from the departure point to the present location, the fuel consumption from several tens of minutes before the present to the present, and the like.

  By performing the above processing by the fuel consumption information processing unit 903, the fuel consumption distribution information for each driving situation pattern can be accumulated in the fuel consumption information 224 in steps 2001 to 2004 of FIG. As an example of the result, as shown in FIGS. 14A, 14 </ b> B, and 14 </ b> C, the frequency distribution of fuel consumption can be obtained for each combination of driving condition parameters. In step 2005, the eco-driving degree can be automatically acquired. The eco-driving degree can be input by the driver himself (this point will be described later). The eco-driving degree is used as data when calculating the fuel consumption amount of the planned travel route (this will be described later).

  Next, the processing of the route search unit 907 will be described with reference to FIG. This process is activated by a route search instruction from the dialogue processing unit 900. FIG. 16 shows an example of the dialogue processing screen. The dialogue processing is performed via the display 208, the operation button group 207, and the remote controller 110. In addition, since a general navigation screen operation method can be used for the operation, a detailed description thereof is omitted. In FIG. 16, step 2501 is activated by operating a search start button 2601. Further, when changing the search condition, a detailed condition setting screen as shown in FIG. 17 can be called by operating the condition setting button 2602. Specific examples of detailed conditions include a driver's unit price per hour, fuel unit price, and eco-driving level. These parameters are used for fuel consumption calculation and cost calculation.

  When the route search is started by the operation of the search start button 2601, the starting point and the destination set via the dialogue processing unit 900 are acquired in step 2501. In step 2502, the detailed condition value preset in the detailed condition setting screen as shown in FIG. 17 is acquired.

  In step 2503, the travel time and fuel consumption are obtained for the route candidate from the departure point to the destination. The calculation process is performed by the predicted fuel consumption calculation unit 910. An overview of the processing flow is shown in FIG. Details of FIG. 20 will be described later.

  In step 2504, using the obtained fuel consumption and travel time, the cost for each road link is calculated with reference to the detailed condition information.

  In step 2505, the route with the lowest cost is searched. As a search method, the Dijkstra method or the like can be used. At this time, it is possible to search not only the route with the lowest cost but also the route with the second or third lowest cost, for example.

  In step 2506, the result obtained in step 2505 is displayed on the screen. An example is shown in FIG. Here, the estimated arrival time, travel time, toll road fee, estimated fuel consumption, and the total cost estimate converted to money are displayed. Furthermore, it is possible to display comparatively the routes with the second and third smallest costs.

  In step 2507, it is determined whether or not the user has operated the route confirmation button 2801 in FIG. When the route confirmation button 2801 is operated to confirm the route, the route search is terminated and route guidance is started. Also, when the condition setting button 2802 in FIG. 18 is operated, the route is not fixed, the detailed condition setting screen as shown in FIG. 17 is displayed, the setting of the detailed condition can be changed, and the route is searched again. .

  On the detailed condition setting screen shown in FIG. 17, for example, hourly unit price information 2701 considered by the driver, fuel unit price information 2702, and the eco-driving degree 2703 that the driver wants to keep in mind can be input. The eco-driving degree 2703 is input with a value from 0 to 10, for example, a feeling to be considered for eco-driving about how much eco-driving the driver performs when traveling to the destination. That is, 10 is set when eco-driving is intended, and 1 is set when driving is more important than fuel consumption. Further, as described above, the eco-driving degree 2703 can be automatically obtained from the latest driving tendency (such as fuel efficiency) by the fuel efficiency information processing unit 903. The most recent driving is, for example, driving from the departure point to the present location, driving from several tens of minutes before the present to the present, and the like. Detailed search conditions such as the set eco-driving degree are stored in the eco-driving degree 904 and the search condition 905. The hourly unit price information 2701 may be input by the driver's actual hourly wage, or may be input by the driver's sensory monetary value. That is, it is possible to make adjustments such that the unit price per hour is reduced when there is enough time, and is increased when busy. The unit price of fuel 2702 may be the sales price of the nearest gas station, or the unit price of gasoline may be collected and automatically set using a mobile phone network, the Internet, or the like. Further, a driver's sensory monetary value may be input.

  Another example of the detailed condition setting screen is shown in FIG. In this example, a value 2902 obtained by estimating the amount of consumed fuel according to the value of the eco-driving degree is displayed under the eco-driving degree slider 2901. Thereby, the effect of eco-driving can be clearly shown to the driver.

  Next, the process of the predicted fuel consumption calculation unit 910 is shown in FIG. The predicted fuel consumption calculation unit 910 is called by the route search unit 907 for each candidate route link in step 2503 of FIG.

  In step 3001, a target link is identified and its road characteristics are read out. The road characteristics are information stored in the map information 220, the traffic congestion level information 222, the traffic information 226, and the route information 228 shown in FIG. 9, and are used to search the driving situation pattern stored in the fuel consumption information 224. use.

  Next, vehicle information is read out in step 3002. The vehicle information is information acquired by a CAN or a sensor as described above. Specifically, information such as on / off of switches such as wiper switch, air conditioner switch, light switch, temperature, weight, driver ID indicating who is driving, etc., the value does not fluctuate every moment by driving It is a parameter. This vehicle information is used to determine the driving situation pattern stored in the fuel consumption information 224. The values of the wiper switch, the air conditioner switch, and the temperature prior to the present time may be set based on a future weather forecast. The light switch may be set based on the travel time.

  In step 3003, a driving situation pattern is determined from the acquired road characteristics and vehicle information, and the fuel consumption distribution in the pattern having the highest correlation with the driving situation pattern is extracted from the information stored in the fuel consumption information 224. Selection of a pattern with high correlation can be performed by the same processing as that in step 2003 described above.

  In step 3004, the estimated fuel consumption is obtained by referring to the set eco-driving degree 904 information from the fuel consumption distribution taken out in step 3003. FIG. 21 shows a method for obtaining the estimated fuel consumption from the eco-driving degree. (A) and (B) are given as examples, but either may be adopted. (A) is a fuel consumption tendency characteristic of the driver specified by the driver ID, and the horizontal axis indicates the fuel consumption distribution and the eco-driving degree, and the vertical axis indicates that the frequency of the fuel consumption associated with the driving history can be updated. It is. The fuel efficiency frequency distribution was created with the eco-driving degree in the range of 0 to 10 with the eco-driving degree being 0 and the eco-driving degree being 10 being the eco-driving degree. Here, the minimum value, the maximum value of fuel consumption, and the distribution between them are determined from the fuel consumption actually obtained in the driving history of the driver, and therefore, the fuel consumption and frequency distribution differ for each driver. In addition, the fuel economy with the highest fuel economy frequency is positioned as the intermediate value (5) of the eco-driving degree. Such a fuel consumption frequency distribution indicates a fuel consumption tendency characteristic for each driver. In (A), an internal division is taken with the set value of eco-driving degree (6.0 in the example in the figure), and the fuel consumption value corresponding to the internal division point is set as the estimated fuel consumption. On the other hand, the fuel consumption tendency characteristics of (B) are a graph showing the fuel consumption distribution on the horizontal axis and the ecological driving degree indicating that the frequency of the fuel consumption accompanying the driving history can be updated on the vertical axis. In (B), the fuel economy frequency distribution is accumulated to make the total eco-driving degree 10 and the fuel economy at the cumulative value corresponding to the set eco-driving degree value (6.0 in the example in the figure) is the estimated fuel economy. To do.

  The fuel consumption tendency characteristic in FIG. 21A is shown by the relationship between the fuel consumption frequency distribution and the eco-driving degree. In this case, the relative position of the eco-driving degree with respect to the fuel consumption frequency distribution changes when the minimum or maximum value of the fuel consumption frequency distribution or the fuel consumption therebetween is changed and updated according to the driving history. As a result, even if the driver's fuel consumption tendency changes, it can be obtained. Therefore, for example, even if the eco-driving degree is input as usual when the driving is in the tendency of reducing the fuel consumption without the driver's knowledge, the fuel consumption of a lower frequency than before is shown. Therefore, when the eco-driving degree is input, the driver knows the change in the fuel consumption tendency by displaying the corresponding fuel consumption or displaying the predicted fuel consumption calculated later based on the fuel consumption. And it makes it possible to correct the driving attitude.

  In the case of FIG. 21B, the same can be said for FIG. That is, in this case, if the fuel consumption tendency changes with the driving history, the accumulated fuel consumption frequency distribution curve (fuel consumption tendency characteristic) changes in accordance with the updated fuel consumption frequency distribution as shown in FIG. Even if the same eco-driving degree is specified, the estimated fuel consumption at that time also changes due to a change in fuel consumption tendency. Thereby, it is possible to recognize a change in the fuel consumption tendency of the driver.

  In step 3005, a fuel consumption amount required for traveling on the link is obtained from the estimated fuel consumption. This is calculated on the assumption that the link length is traveled with the estimated fuel consumption.

  In step 3006, the travel time of the link is obtained. If the link is predicted to be congested or congested, the travel speed depends mainly on the surrounding traffic conditions, so the link predicted by the traffic / travel time information 1030 provided by the information providing center 106 (see FIG. 4). Use travel time. On the other hand, when it is predicted that the vehicle is going smoothly, the travel speed changes according to the eco-driving degree reflecting the driver's intention, and the link travel time is corrected in consideration of this. As in the fuel efficiency estimation, the correction can be performed by storing the average hourly speed distribution for each driving condition pattern during smooth running and estimating by the eco-driving degree. Similar to fuel efficiency estimation, road condition information such as slope, speed limit, road type, curve frequency, number of traffic lights, etc., and driver ID, wiper switch, light switch, etc. Information.

  In step 3007, the fuel consumption and travel time obtained above are shown to the route search unit 907.

  The embodiment of the present invention has been described above. According to the car navigation device of the above embodiment, it is possible to perform route search with the optimum cost in consideration of the characteristics of individual driving.

  The second embodiment will be described below. This embodiment is intended to provide guidance on eco-driving using the fuel consumption estimation system of the first embodiment.

  The system configuration of this embodiment is the same as that of the first embodiment. In this embodiment, the system of the first embodiment has a function of storing the eco-driving history in the storage device 210 (FIG. 6) and a function of displaying a history graph on the display 208 with reference to the stored history data. Is newly provided. As shown in FIG. 22, the history graph displays the history of the eco-driving degree obtained in the driving so far. At this time, information on the minimum fuel consumption and the maximum fuel consumption of the user can be displayed. A warning is displayed when the latest fuel consumption is worse than the minimum fuel consumption, and a compliment is displayed when the fuel consumption exceeds the maximum fuel consumption. Thereby, it becomes possible to confirm the past driving history, and it is possible to perform eco driving guidance according to the characteristics of each driver.

  As another example, during the route guidance, as shown in FIG. 23, the function of displaying the current eco-driving level in real time on the display 208 and the estimation of the fuel consumption based on the current eco-driving level are repeated. Provide a function to be executed. It is also possible to compare the fuel consumption estimated from the target eco-driving level with the fuel consumption estimated from the current actual eco-driving level and display the comparison result. In the example shown in FIG. 23, it is displayed that the fuel consumption estimated from the current eco-driving degree is 1.2 liters less than the fuel consumption estimated from the target eco-driving degree. Thereby, it is possible to immediately grasp the eco-driving effect with respect to the target eco-driving degree.

  The third embodiment will be described below. Similar to the second embodiment, this embodiment is intended to provide guidance on eco-driving by using the fuel consumption estimation system of the first embodiment.

  The system configuration of this embodiment is the same as that of the first embodiment. In this embodiment, time information such as a date is added to the fuel consumption information 224 (FIG. 9) in the system of the first embodiment. Further, referring to the fuel consumption information 224 to which the time information is added, the transition of the fuel consumption distribution over time is displayed on the display 208. A display example of the transition of the fuel consumption distribution is shown in FIG. In this example, fuel consumption information is collectively stored every month, and an example is shown in which each is superimposed and displayed. This embodiment makes it possible to clearly indicate how the driving tendency has changed.

  The fourth embodiment will be described below. Similar to the second embodiment, this embodiment is intended to provide guidance on eco-driving by using the fuel consumption estimation system of the first embodiment.

  The system configuration of this embodiment is the same as that of the first embodiment. In this embodiment, a function for transmitting fuel consumption information 224 to the information providing center 106 via the traffic information acquisition unit 911 is provided in the system of the first embodiment. The information providing center 106 passes the received fuel consumption information to the fuel consumption information analysis unit 320 (see FIG. 3). The fuel consumption information analysis unit 320 assigns vehicle identifiers to the fuel consumption information sent from a plurality of vehicles, and stores them in the fuel consumption information DB 321.

  In response to a request from the user, the fuel consumption information analysis unit 320 refers to the fuel consumption information collected from a plurality of vehicles stored in the fuel consumption information DB, creates fuel consumption distribution data for the entire plurality of vehicles, and returns it to the requesting system. To do. In the request source system, the fuel consumption distribution information of the host vehicle and the fuel consumption distribution information of the entire vehicle are superimposed and displayed on the display 208. An example of the result is shown in FIG. Thereby, it becomes possible to grasp the difference in the way of driving between oneself and another driver. In particular, since the fuel consumption distribution is obtained for each driving situation pattern, comparison under the same driving situation can be performed, and unfairness of the evaluation can be reduced.

  The fifth embodiment will be described below. In the first to fourth embodiments, the basic data for estimating the fuel consumption amount on the planned travel route is the fuel consumption frequency distribution data (fuel consumption tendency characteristic) stored in the fuel consumption information 224. In the present embodiment, the fuel consumption is divided into the following four factors. That is, the energy required for driving the engine itself, the energy required to travel against road surface rolling resistance and gradient resistance, the energy required to travel against air resistance, and the vehicle are accelerated. It is assumed that fuel is consumed by the energy required for this. When separated into these four factors, the fuel consumption Q is expressed by the following (formula 1). This model is equivalent to that described in Equation 17 of Non-Patent Document 1 below.

Takashi Oguchi, Masahiko Katakura, Masaaki Taniguchi, "Models for Estimating Carbon Dioxide Emissions from Urban Road Traffic" Proceedings of Japan Society of Civil Engineers No.695 / IV-54, pages 125-136, January 2002 Q = Qidle + Qmove + Qair + Qacc (Equation 1) Qidle = fidle * T Qmove = C * M * g (μ * D + h) Qair = C * k * Σ (v (t) * v (t) * v ( t)) Qacc = C * (M + m) * aee aee = Σ (1/2 (v (t) * v (t)-v (t-1) * v (t-1)))) * δ , Δ is 1 when v (t)> v (t-1), and 0 otherwise. * Is multiplication.

  Here, Qidle is the amount of fuel used to drive the engine itself, Qmove is the amount of fuel used to drive against road surface rolling resistance and gradient resistance, and Qair is used to drive against air resistance. The amount of fuel used, Qacc, is the amount of fuel used to accelerate the vehicle. Also, fidle is the fuel consumption at idling per unit time, T is the measurement time, C is a coefficient that converts the unit energy amount into fuel consumption, M is the vehicle weight, g is the gravitational acceleration, μ is the rolling friction coefficient, D is the distance traveled, h is the altitude difference of the climb slope, k is the air resistance coefficient, v is the speed, m is the weight equivalent to the rotating part during acceleration, aee is the acceleration energy equivalent, and t is the time when the speed v is sampled Yes.

  In the above (Expression 1), the gravitational acceleration g is a known value, and fidle, C, M, μ, k, and m are known values specific to the vehicle. Furthermore, if a travel route is given, the values of D and h are determined. Further, when the traffic information processing unit 307 predicts the traffic situation, if the speed for each link can be predicted, the average of T and v can be obtained. The unknown value is aee, which is determined from the driver's driving characteristics and surrounding traffic conditions. If aee is determined, the fuel consumption Q can be obtained from the above (Equation 1).

  The value of aee is determined according to the frequency and maximum speed because the vehicle is repeatedly stopped and started in accordance with the surrounding traffic conditions in a traffic jam. Further, when the road is in a smooth running state, it can be determined based on driving characteristics reflecting the driver's intention. That is, aee becomes large in a driver who accelerates rapidly or performs a wave-like driving without maintaining a constant speed. Therefore, as the attribute for determining aee, it is possible to adopt road conditions, in this case, the degree of congestion, predicted speed, curve frequency, number of traffic lights, road width, weather condition, time zone, and the like. It is possible to perform fuel estimation that reflects individual driving characteristics by acquiring attributes indicating the conditions of these roads, classifying them by road condition pattern, and storing the obtained aee as characteristics of the corresponding road condition pattern. It becomes possible.

  Calculation of aee data is performed as follows. The calculation process is executed in step 2001 in FIG. In step 2001, the speed is sampled every unit time and compared with the speed at the previous sampling. If it is accelerating (v (t)> v (t-1)), the amount of change energy is integrated over the measurement time T using the aee equation of (Equation 1). At this time, the acceleration due to the dead weight on the downhill must be excluded when calculating the fuel consumption. For this purpose, the accelerator opening is monitored, and when the accelerator opening is 0 (when the accelerator is not depressed), the acceleration that occurs naturally is calculated from the difference in altitude and the rolling friction coefficient. However, when the speed change is small, it is possible to take a method of not integrating. The obtained aee is stored in the fuel efficiency information 224 as frequency distribution data for each driving situation pattern by the fuel efficiency information processing calculation unit 903 in the process from step 2003 to step 2005 in FIG. 10 as in the first to fourth embodiments. Is done.

  Using the aee data thus obtained, the fuel consumption amount Q obtained by (Equation 1) is acquired as fuel consumption information. As a result, it is possible to extract only the component that greatly depends on the individual driving characteristics such as acceleration of the vehicle by the driver from the fuel consumption amount Q. That is, it is possible to evaluate the fuel consumption by excluding components other than acceleration by the driver, such as a difference in altitude of mountain roads and air resistance on a highway, from the evaluation target.

  In this embodiment, the fuel consumption amount estimation process (step 2503 in FIG. 15) in the route search from the departure place to the destination is performed by the predicted fuel consumption calculation unit 910 as follows. This process is almost the same as the process flow diagram of FIG. In FIG. 20, the fuel consumption is estimated according to the eco-driving degree from the fuel consumption frequency distribution that matches the driving situation pattern. In this embodiment, aee is estimated according to the eco-driving degree from the distribution of aee that matches the driving situation pattern.

  In step 3003 of FIG. 20, a driving situation pattern is determined from the acquired road characteristics and vehicle information, and the frequency distribution of aee in the pattern having the highest correlation with this driving situation pattern is obtained from the information stored in the fuel consumption information 224. Take out. Selection of a pattern with high correlation can be performed by the same processing as in Step 2003 described above.

  In step 3004, the estimated value of aee is obtained from the distribution of aee extracted in step 3003 with reference to information on the set eco-driving degree 904. The method for obtaining aee from the eco-driving degree is the same as the method shown in FIG. In FIG. 21, the estimated fuel consumption is obtained using the fuel consumption distribution. In this embodiment, aee is estimated using the aee distribution.

  Next, Qidle is calculated from the estimated travel time of the link. Find Qmove from link length and altitude difference. Qair approximates the average speed by dividing the link distance by the travel time. Qacc is obtained using the estimated aee. The estimated fuel consumption amount is obtained by adding the values of the factor separation data obtained as described above.

  A sixth embodiment will be described. In this embodiment, factor separation data for fuel consumption in the fifth embodiment is stored as a history for each unit time or for each unit travel distance. The fuel consumption information processing calculation unit 903 obtains aee by the process of step 2001 in the fifth embodiment, and obtains Qacc from this value. Furthermore, Qidle, Qmove, and Qair can be obtained by using the difference in altitude between the speed and the climbing gradient and the known variables shown in (Equation 1). The Qidle, Qmove, Qair, and Qacc are stored in the eco-driving degree 904 for each unit time or for each unit distance.

  When the user requests history display via the dialogue processing unit 900, the dialogue processing unit 900 refers to the data stored in the eco-driving degree 904, and displays the fuel as shown in FIG. A consumption history graph 2601 is displayed. The fuel consumption history graph 2601 displays a time series graph in which Qidle 2602, Qmove 2603, Qair 2604, and Qacc 2605 are stacked for each unit time or for each unit distance. When the history display for each distance is performed, for example, when there is a lot of traffic, Qidle increases, Qmove increases when traveling on a mountain road, and Qair increases when traveling at high speed. Further, for example, when the operation becomes rough due to heavy use of rapid acceleration or the like, Qacc increases. Thus, by viewing this history display, the user can grasp not only the increase / decrease history of the fuel consumption but also the cause thereof, and can reflect on the past operation and reflect it.

  This history display may be performed not only for each unit time or unit distance but also for each day or each user reset timing. In this case, it is also possible to normalize and display each integrated value by distance or time. This makes it possible to compare past histories as driving tendencies.

  Each process performed by the car navigation device described above may be performed by a server such as an information providing center. Then, the car navigation device may obtain and display information processed by the server.

  The above-described embodiment can be variously modified within the scope of the gist of the present invention. For example, in the above description, an example in which the present invention is applied to a car navigation device has been described. However, the present invention can also be applied to a navigation device other than an on-vehicle device.

The effects of the above-described embodiment are summarized and listed as follows.
(1) The driver's individual fuel consumption tendency according to the past vehicle situation and route situation can be acquired, and further, the driver's driving tendency on the planned driving route by taking into account the current driving tendency It becomes possible to estimate the fuel consumption reflecting the above.
(2) The driver's fuel consumption tendency is a parameter that affects the fuel consumption. One or more of vehicle weight, light lighting state, wiper operation state, air conditioner operation state, air temperature, driver ID In addition, the route status information uses one or more information of road types such as expressways and ordinary roads, speed limit, gradient information, curve curvature information, the number of traffic lights, etc. This makes it possible to estimate the fuel consumption of the planned travel route with high accuracy.
(3) The fuel consumption tendency of the driver is searched for the fuel consumption frequency distribution of the driving situation pattern having a high degree of correlation from the fuel consumption frequency distribution for which the latest fuel consumption value is obtained in advance, and at which position ( It is possible to automatically estimate the eco-driving degree by determining whether it corresponds to the eco-driving degree).
(4) When the driver sets the eco-driving degree on the dialogue screen, it is possible to predict the fuel consumption amount that directly reflects the driver's fuel consumption tendency on the planned travel route. In addition, by directly inputting the eco-driving degree, the driver's awareness is also effective.
(5) Using the above fuel consumption estimation process, the amount of fuel consumed for a plurality of routes from the current location to the destination can be calculated, and the minimum route or the ranked route can be displayed. It is possible to estimate the energy saving route and cost according to the consciousness, and to select the route.
(6) The fuel, time, and toll road toll can be comprehensively evaluated, and the route with the lowest cost or the route ranked according to the driver's awareness can be selected.
(7) By providing the step of dividing the fuel efficiency frequency distribution information for each set period and displaying it in time series, it is possible to present how the driving tendency has changed.
(8) By collecting fuel frequency distribution information from a plurality of vehicles and comparing the fuel consumption distribution information of a specific vehicle, the characteristics of the specific vehicle can be displayed, and a relative driving evaluation can be performed. Is possible.

  In the above embodiment, the fuel consumption amount of the planned travel route is estimated by obtaining the fuel consumption tendency of the driver according to the driving situation, but the present invention assumes that the following simple fuel consumption estimation system is assumed. Is also applicable. For example, if the planned travel route is a commuting course during the commuting time period, transportation, route bus, etc., on a daily basis or on a regular basis, the overall fuel consumption and travel distance of the travel route are considered without considering the driving situation. Therefore, it is also possible to capture the average fuel consumption for the entire travel route, express this as a fuel consumption frequency distribution accompanying the driving history, and obtain the fuel consumption tendency characteristic in relation to the eco-driving degree based on this. Even in such a simple fuel consumption estimation system, the fuel consumption estimation accuracy can be improved according to the fuel consumption tendency of the driver as long as it is a daily and steady travel route.

1 is a schematic configuration diagram of a car navigation system to which an embodiment of the present invention is applied. It is a schematic block diagram of an information provision center. It is a functional block diagram of an information provision center. It is a figure which shows the structural example of the information downloaded from an information provision center to a navigation apparatus. It is a figure for demonstrating traffic jam and travel time information. It is a schematic block diagram of a car navigation apparatus. (A) is a figure which shows the structural example of the node data contained in map information, (B) is a figure which shows the structural example of the link data contained in map information. It is an example of the data graph which shows traffic congestion level information. It is a functional block diagram of a car navigation apparatus. It is a flowchart which shows the process of an operation | movement of a fuel consumption information processing calculating part. It is a figure which shows an example of vehicle information. It is a time series graph which shows an example of vehicle information. It is a figure for demonstrating the relationship between a driving condition pattern and fuel consumption distribution. It is a figure which shows an example of the change of fuel consumption distribution when a driving condition pattern changes. It is a flowchart which shows the process of a route search part. It is an example of a display on a display of a destination setting screen. It is an example of a display on the display of a detailed condition setting screen. It is a display example on the display of the cost estimation screen of the route search result. It is another example of the example of a display on the display of a detailed condition setting screen. It is a flowchart which shows the process of the prediction fuel consumption calculating part. It is a figure for demonstrating the process at the time of calculating | requiring estimated fuel consumption by eco-driving degree from fuel consumption distribution. It is a display example on the display of the eco-driving history screen. It is an example of a display on a display of a re-evaluation screen of fuel consumption by eco-driving degree. It is an example of a display on the display of a fuel consumption distribution history screen. It is the example of a display on the display of the screen which compared fuel consumption distribution with the own vehicle and the whole vehicle. It is an example of a display on the display of a fuel consumption history screen.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 101 ... Car navigation apparatus, 102 ... Mobile phone, 103 ... GPS receiver, 104 ... Base station, 105 ... Network, 106 ... Information provision center, 107 ... Traffic information center, 108 ... Dedicated line, 110 ... Remote control, 201 ... Car Navigation device CPU, 202 ... RAM, 203 ... flash ROM, 204 ... cell phone IF (interface), 205 ... remote receiver, 206 ... sensor IF, 207 ... operation buttons, 208 ... display, 209 ... car navigation device Bus, 210 ... Storage device, 220 ... Map information, 222 ... Congestion level information, 224 ... Fuel consumption information, 226 ... Traffic information, 228 ... Route information, 302 ... Request accepting unit, 303 ... Information providing unit, 304 ... Map providing unit 305 ... Fuel consumption analysis result providing unit 306 ... Traffic information provision 307: Traffic information processing unit, 308 ... Traffic information acquisition unit, 310 ... Map information DB, 311 ... Fuel consumption analysis result DB, 312 ... Traffic information DB, 313 ... Predicted traffic information DB, 320 ... Fuel consumption information analysis unit, 321 ... Fuel efficiency information DB, 401 ... CPU of information providing center, 402 ... Memory, 403 ... External storage device, 404 ... Bus of information providing center, 405 ... Communication device 1, 406 ... Communication device 2, 407 ... Input device, 408 ... Output 409 ... reading device, 410 ... storage medium, 900 ... interaction processing unit, 903 ... fuel consumption information processing calculation unit, 907 ... route search unit, 910 ... predicted fuel consumption calculation unit, 911 ... traffic information acquisition unit.

Claims (18)

In the fuel consumption estimation system for automobiles,
In addition to obtaining the fuel consumption and travel distance associated with the driving history from the vehicle, information on the driving situation including at least one of the vehicle state information and the traveling route situation information accompanying the driving history is obtained and based on the information The fuel consumption frequency distribution for each driving situation pattern is obtained, and based on the fuel consumption frequency distribution, the individual fuel consumption tendency of the vehicle driver is obtained for each driving situation pattern, and the vehicle travels based on the fuel consumption tendency for each driving situation pattern. A fuel consumption estimation system for an automobile characterized by predicting fuel consumption of a planned route. The fuel consumption estimation system for automobiles according to claim 1,
When predicting the fuel consumption of the planned travel route, a search is made for a driving situation pattern having a high correlation with the route information of the link for each link of the planned traveling route, and the fuel consumption tendency of the driving situation pattern is used as a basis. A fuel consumption estimation system for an automobile characterized by predicting the fuel consumption of a planned travel route. The fuel consumption estimation system for automobiles according to claim 1,
The vehicle state is at least one of vehicle weight, light lighting state, wiper operation state, air conditioner operation state, temperature, and driver ID.
The route status information is at least one of a road type such as an expressway and a general road, speed limit, gradient information, curve curvature information, and the number of traffic lights. The fuel consumption estimation system according to any one of claims 1 to 3,
The fuel consumption tendency for each driving situation pattern is composed of the fuel consumption frequency distribution created for each driving situation pattern, and the driver's fuel consumption tendency is searched for a driving situation pattern having a high correlation with the latest driving situation at the time of driving. It is estimated from the fuel consumption frequency distribution of this driving situation pattern, this estimation is determined by the position in the searched fuel consumption frequency distribution where the latest fuel consumption is located, and this position is correlated with the planned travel route. A fuel consumption estimation system which is applied to a fuel efficiency frequency distribution of a high driving situation pattern and estimates a fuel efficiency tendency. The fuel consumption estimation system according to any one of claims 1 to 3,
The fuel economy tendency of the driver relates the fuel efficiency frequency distribution created along with the driving history and the economic driving degree, and when the driver sets the economic driving degree on the dialogue screen, the corresponding fuel consumption is A fuel consumption estimation system that is determined from a fuel consumption frequency distribution and predicts a fuel consumption of a planned travel route based on the selected fuel consumption. In the fuel consumption estimation system for automobiles,
Information related to the driving situation including at least one of the vehicle information accompanying the driving history and the status information of the route traveled in addition to acquiring the fuel consumption and the traveling distance as information necessary for obtaining the fuel consumption accompanying the driving history from the vehicle A fuel consumption information processing unit for obtaining a fuel consumption tendency characteristic of the individual driver of the vehicle based on the information for each driving situation pattern;
A storage unit for storing the fuel consumption tendency characteristics for each driving situation pattern;
An economic driving degree setting unit for setting an economic driving degree targeted by the driver through an input or estimating means;
A predicted fuel consumption calculation unit that predicts the fuel consumption of the planned travel route from at least the set economic driving degree and the fuel consumption tendency characteristic of the driver,
An automobile fuel consumption estimation system comprising: The fuel consumption estimation system for automobiles according to claim 6,
The fuel consumption information processing calculation unit creates an update of the individual fuel consumption frequency distribution based on at least the travel distance and fuel consumption information associated with the driving history acquired from the vehicle, and the economic driving degree is included in the frequency distribution. A fuel consumption estimation system for an automobile, characterized in that the fuel consumption tendency characteristic is obtained by associating the vehicle with each other. The fuel consumption estimation system for automobiles according to claim 6,
The fuel consumption information processing calculation unit creates an update of the individual fuel consumption frequency distribution based on at least the travel distance and fuel consumption input data associated with the driving history acquired from the vehicle, and the fuel consumption frequency distribution includes A fuel consumption estimation system for an automobile, characterized in that the fuel consumption tendency characteristic is obtained by accumulating the values and the economic driving degree. The fuel consumption estimation system for automobiles according to claim 6,
The fuel consumption information processing calculation unit can update the driver's individual fuel consumption frequency distribution for each driving situation pattern based on at least the driving situation, travel distance, and fuel consumption input data associated with the driving history acquired from the vehicle. Create the fuel consumption tendency characteristics by associating the fuel consumption frequency distribution for each driving situation pattern with the economic driving degree,
The storage unit stores fuel consumption tendency characteristics for each driving situation pattern,
The predicted fuel consumption calculation unit obtains a fuel consumption tendency characteristic of a driving situation pattern having a high correlation with the road information related to a planned travel route from the storage unit, and sets the acquired fuel consumption tendency characteristic and the set fuel consumption tendency characteristic. A fuel consumption estimation system for an automobile, which predicts the fuel consumption of a planned travel route from the degree of economic driving. The fuel consumption estimation system for automobiles according to claim 6,
The fuel consumption information processing calculation unit can update the driver's individual fuel consumption frequency distribution for each driving situation pattern based on at least the driving situation, travel distance, and fuel consumption input data associated with the driving history acquired from the vehicle. Create the fuel consumption trend characteristics by accumulating the fuel consumption of the fuel consumption frequency distribution for each driving situation pattern and associating it with the economic driving degree,
The storage unit stores fuel consumption tendency characteristics for each driving situation pattern,
The predicted fuel consumption calculation unit obtains a fuel consumption tendency characteristic of a driving situation pattern having a high correlation with the road information related to a planned travel route from the storage unit, and sets the acquired fuel consumption tendency characteristic and the set fuel consumption tendency characteristic. A fuel consumption estimation system for an automobile, which predicts the fuel consumption of a planned travel route from the degree of economic driving. The fuel consumption estimation system for automobiles according to any one of claims 6 to 10,
A fuel consumption system for an automobile, wherein when the economic driving degree is set through the estimating means, the economic driving degree of the driver is estimated from a fuel consumption tendency associated with the latest driving history. The automobile fuel consumption estimation system according to claim 9 or 10,
The driving situation acquired from the vehicle is vehicle information on the engine load obtained from various sensors during vehicle travel and road information on the travel route,
The automobile fuel consumption estimation system, wherein the road information of the travel route is road information including at least a link length for each link in a unit between road intersections. The fuel consumption estimation system for automobiles according to claim 12,
The vehicle information related to the engine load is at least one of the weight of the vehicle, the lighting state of the light, the operating state of the wiper, the operating state of the car air conditioner, the temperature, and the driver ID.
The road information of the driving route is at least one of a road type including a highway and a general road, speed limit, gradient information, curve curvature information, and the number of traffic lights. In any one of Claims 6 thru | or 10,
The economic driving degree is set by a driver through an interactive screen. Utilizing the fuel consumption estimation system according to any one of claims 1 to 14,
Calculating the amount of fuel consumed for a plurality of scheduled routes from the current location to the destination;
A step of displaying a route in which the fuel consumption is minimized or a route in which the fuel consumption is ranked with respect to the fuel consumption of the plurality of planned traveling routes obtained;
The route search system characterized by having. The route search system of claim 15, further comprising:
Means for entering fuel unit price;
A means to enter a unit price per hour to estimate the driver's cost per hour;
Means for converting the fuel consumption obtained by the fuel consumption estimation system into a monetary amount based on the input fuel unit price;
Steps to find travel time for each route according to driver fuel economy trends and traffic conditions,
A step of converting the obtained travel time into a monetary amount based on the input hourly unit price;
Obtaining tolls for toll roads along the route;
Summing the amount of money required to travel each route, displaying the least amount route or ranked route;
The route search system characterized by having. Utilizing the fuel consumption estimation system according to any one of claims 1 to 14,
A driving guidance system comprising means for dividing fuel efficiency distribution information for each set period and displaying the information in time series. Utilizing the fuel consumption estimation system according to any one of claims 1 to 14,
Means for collecting fuel consumption distribution information accumulated for each vehicle from a plurality of vehicles;
Means for comparing fuel consumption distribution information collected from the plurality of vehicles with fuel consumption distribution information of the specific vehicle, and displaying a difference in distribution between the specific vehicle and other vehicles;
A driving guidance system characterized by comprising:

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