JP4637073B2 - Navigation device, method and program - Google Patents

Description

  The present invention relates to a vehicle navigation apparatus.

  A vehicle navigation device is a device that supports driving of a user (driver) by providing route guidance from a departure place to a destination. In such a vehicle navigation apparatus, when a user sets a departure point and a destination, a search for a travel route from the departure point to the destination is performed. Then, the searched travel route is displayed on the screen, and voice guidance according to the travel route is notified to the user. Thus, in the vehicle navigation apparatus, route guidance information related to the travel route is provided to the user who is driving the vehicle.

  However, in recent vehicle navigation devices, in addition to route guidance information, various information such as restaurant information, traffic jam information, and new DVD information is provided to the user. When providing such a large amount of information to the user, the workload (cognitive load) of the user driving the vehicle becomes a problem. In other words, when a user (driver) drives a vehicle, when performing a driving operation requiring attention (for example, turning right or changing lanes), the user's workload is high, and therefore it is not suitable as a timing for providing information. On the other hand, when a user (driver) drives a vehicle, when performing a driving operation that does not require much attention (for example, going straight, waiting for a signal, etc.), the user's workload is low, which is suitable as a timing for providing information. .

Therefore, conventionally, a technique for estimating the workload of a user driving a vehicle and prioritizing information provided to the user is known (see, for example, Patent Document 1). In this conventional technique, the current workload is estimated by inferring the user's state using various sensors. When the current workload is high, the provision of information (incoming mobile phone calls) is restricted.
JP-T-2004-524203 (page 4-16, FIG. 1)

  However, in the above conventional technique, since it is determined whether to provide information by estimating the current workload, it is possible to make a judgment only on the spot about the timing of providing information, and to some extent ahead There was a problem that information could not be provided to users with planning until the future. For example, even if there is information to be provided by a certain point (halfway point), if other information is provided to the user first, the timing for providing the information may be missed. As described above, there is a problem that information cannot be provided to the user at an optimal timing.

  The present invention has been made to solve the above-described conventional problems, and an object of the present invention is to provide a navigation device that can provide information to a user at an optimal timing with a plan to the future.

  The navigation device according to the present invention includes a travel route data storage unit that stores travel route data indicating a travel route on which a vehicle driven by a user travels, and a correlation between the user's behavior and the workload while the vehicle is traveling. Correlation data storage means for storing the workload correlation data to be shown, provision schedule information storage means for storing provision schedule information which is information scheduled to be provided to the user when traveling on the travel route, and the travel route Based on data, user behavior estimation processing means for performing user behavior estimation processing for estimating the behavior of the user traveling on the travel route, and based on the estimated user behavior and the workload correlation data, A workload for performing a workload prediction process for predicting a workload corresponding to the user's behavior while traveling on a travel route Measurement processing means, scheduling processing means for performing a scheduling process for determining a timing for providing the provision schedule information to the user when traveling on the travel route based on the predicted workload, and the provision at the timing Information output means for outputting schedule information to the user.

  Thereby, the user's behavior when traveling on the planned travel route is estimated, the workload corresponding to the user's behavior is predicted, and the timing for providing the provision schedule information is determined based on the predicted workload. be able to. Thus, by predicting the future workload, it is possible to provide information to the user at an optimal timing with planning to the future.

  In the navigation device of the present invention, the workload correlation data is a driving workload correlation indicating a correlation between a driving operation when the user drives the vehicle and a driving workload caused by the driving operation. The user behavior estimation processing means estimates the driving operation of the user traveling on the travel route based on the travel route data, and the workload prediction processing means estimates the user's driving Based on the operation and the driving workload correlation data, the driving workload corresponding to the driving operation of the user traveling on the travel route is predicted, and the scheduling processing means is based on the predicted driving workload. The timing for providing the provision schedule information to the user traveling on the travel route is determined.

  Thus, the driving operation of the user when traveling on the planned driving route is estimated, the driving workload corresponding to the driving operation of the user is predicted, and the provision schedule information is provided based on the predicted driving workload. Timing can be determined. Thus, by predicting the future driving workload, it is possible to provide information to the user at an optimal timing with a plan to the future.

  In the navigation device of the present invention, the workload correlation data indicates a correlation between a device operation when the user operates a device provided in the vehicle and a device operation workload caused by the device operation. The user behavior estimation processing means estimates the equipment operation of the user traveling on the travel route based on the travel route data, and the workload prediction processing means Predicting a device operation workload corresponding to the device operation of the user traveling on the travel route based on the estimated device operation of the user and the device operation workload correlation data, and the scheduling processing means, Based on the predicted device operation workload, the provision schedule information is provided to the user traveling on the travel route. To determine the timing to provide.

  Thus, the user's device operation when traveling on the planned travel route is estimated, the device operation workload corresponding to the user's device operation is predicted, and the provision schedule information is obtained based on the predicted device operation workload. The timing to provide can be determined. Thus, by predicting the future equipment operation workload, it is possible to provide information to the user at an optimal timing with a plan to the future.

  Further, in the navigation device of the present invention, the workload correlation data is a visual workload correlation indicating a correlation between a visual action that the user uses vision and a visual workload caused by the visual action. The user behavior estimation processing means estimates a visual motion of the user traveling on the travel route based on the travel route data, and the workload prediction processing means estimates the user's estimated Based on the visual motion and the visual workload correlation data, a visual workload corresponding to the visual motion of the user traveling along the travel route is predicted, and the scheduling processing means is configured to predict the predicted visual A timing for providing the provision schedule information to the user traveling on the travel route based on a dynamic workload That.

  As a result, the user's visual motion when traveling on the planned travel route is estimated, the visual workload corresponding to the user's visual motion is predicted, and the provision is made based on the predicted visual workload The timing for providing information can be determined. Thus, by predicting the future visual workload, it is possible to provide information to the user at the optimal timing with the planability to the future.

  Further, in the navigation device of the present invention, the workload correlation data is an auditory workload correlation indicating a correlation between an auditory motion in which the user uses hearing and an auditory workload caused by the auditory motion. The user behavior estimation processing means estimates the auditory motion of the user traveling on the travel route based on the travel route data, and the workload prediction processing means estimates the user's estimated Based on the auditory movement and the auditory workload correlation data, the auditory workload corresponding to the auditory movement of the user traveling along the travel route is predicted, and the scheduling processing means is configured to predict the auditory workload. A timing for providing the provision schedule information to the user traveling on the travel route based on a dynamic workload That.

  As a result, the user's auditory motion when traveling along the scheduled driving route is estimated, and the auditory workload corresponding to the user's auditory motion is predicted, and the provision is made based on the predicted auditory workload. The timing for providing information can be determined. In this way, by predicting the future auditory workload, information can be provided to the user at the optimal timing with planning to the future.

  In the navigation device of the present invention, the workload correlation data is a psychological workload indicating a correlation between a situation when the user drives the vehicle and a psychological workload caused by the situation. Including correlation data, the user behavior estimation processing means estimates a situation when the user drives the vehicle on the travel route based on the travel route data, and the workload prediction processing means estimates Predicting a psychological workload corresponding to a situation when the user drives the vehicle on the travel route based on the user situation and the psychological workload correlation data, and the scheduling processing means Is based on the predicted psychological workload, the provision schedule information to the user traveling on the travel route To determine the timing to provide.

  Thus, the user's situation when traveling on the planned driving route is estimated, the psychological workload corresponding to the user's situation is predicted, and the provision schedule information is obtained based on the predicted psychological workload. The timing to provide can be determined. Thus, by predicting the future psychological workload, it is possible to provide information to the user at the optimal timing with the planability to the future.

  In addition, the navigation device of the present invention includes behavior characteristic data storage means for storing behavior characteristic data indicating the behavior characteristics of the user during vehicle travel, and the user behavior estimation processing means includes the behavior characteristic data and the travel route. Based on the data, the user's behavior while traveling along the travel route is estimated.

  Thereby, it is possible to estimate the behavior of the user traveling on the travel route in consideration of the behavioral characteristics of the user. Therefore, the accuracy when estimating the user's behavior is improved.

  Further, in the navigation device of the present invention, the behavior characteristic data includes driving skill data indicating the driving skill of the user, and the workload prediction processing means is based on the driving skill data and the travel route data. The workload corresponding to the behavior of the user traveling on the travel route is predicted.

  Thereby, a user's workload when driving | running a driving | running route can be estimated in consideration of a user's driving skill. Therefore, the accuracy when predicting the workload is improved.

  In addition, the navigation device of the present invention is configured to use interest characteristic data storage means for storing interest characteristic data indicating the user's interest characteristic, and when traveling along the travel route based on the interest characteristic data and the travel route data. Information selection means for selecting the provision schedule information to be provided to the user.

  This makes it possible to select provision schedule information to be provided to the user when traveling along the travel route in consideration of the user's interest characteristics. That is, only information of interest to the user (information that the user needs) is selected and provided to the user. Further, by selecting the provision schedule information in this way, it is possible to reduce the information amount of the provision schedule information handled in the scheduling process, and it is possible to reduce the time required for the scheduling process.

  In the navigation device of the present invention, the user behavior estimation processing means estimates the behavior of the user traveling on the partial travel route for the partial travel route to the midpoint of the travel route, and the workload prediction The processing means predicts a workload corresponding to the action of the user traveling on the partial travel route.

  As a result, the user behavior estimation process and the workload prediction process are performed only for the partial travel route that is a part of the travel route. Therefore, the time required for these processes can be shortened as compared to the case where the user behavior estimation process and the workload prediction process are performed for the entire travel route.

  The navigation device of the present invention further includes event detection means for detecting that a predetermined event has occurred while traveling on the travel route, and when the event has occurred, the user behavior estimation processing means is The behavior estimation processing is performed again, and the workload prediction processing means performs the workload prediction processing again.

  As a result, when a predetermined event occurs while traveling on the travel route, the user behavior estimation process and the workload prediction process are performed again. For example, the user behavior estimation process and the workload prediction process are performed again when the vehicle driven by the user passes an intermediate point (the end point of the partial travel route) or when the vehicle driven by the user deviates from the travel route. . As described above, even when a predetermined event occurs while traveling on the travel route, the user behavior estimation process and the workload prediction process are performed again to determine (change) the timing for providing the provision schedule information. Can do.

  In the navigation device of the present invention, the scheduling processing means determines the timing so as to provide the provision schedule information when the predicted workload is equal to or lower than a predetermined workload level, and the predicted When the workload is higher than the workload level, the timing is determined so as not to provide the provision schedule information.

  Thereby, the provision schedule information can be provided to the user at an optimum timing in consideration of the predicted workload. For example, when a user performs a driving operation requiring attention (for example, turning right or changing lanes), the predicted workload of the user is high. At such timing, provision schedule information is not provided to the user. On the other hand, when the user performs a driving operation that requires less attention (for example, going straight ahead or waiting for a signal), the predicted user workload is low. The provision schedule information is provided to the user at such timing. In this way, provision schedule information is provided to the user at an optimal timing.

  In the navigation device of the present invention, the provision schedule information includes a plurality of provision schedule information blocks, each of the provision schedule information blocks includes constraint condition data relating to a workload amount, and the scheduling processing unit includes: The scheduling process is performed for each provision schedule information block, and the timing is determined and predicted so as to provide the provision schedule information block when the predicted workload is equal to or less than the workload amount. When the workload is higher than the workload amount, the timing is determined so as not to provide the provision schedule information block.

  As a result, the provision schedule information can be provided to the user at an optimum timing in consideration of the workload amount of the constraint condition for each provision schedule information block. When the workload amount of the provision schedule information block is higher than the predicted user workload, the provision schedule information block is not provided to the user. On the other hand, when the workload amount of the provision schedule information block is lower than the predicted user workload, the provision schedule information block is provided to the user. In this way, provision schedule information is provided to the user at an optimal timing.

  The navigation method of the present invention performs a user behavior estimation process for estimating the behavior of the user traveling on the travel route based on travel route data indicating a travel route on which the vehicle driven by the user is traveling, Based on the workload correlation data indicating the correlation between the user's behavior during travel and the workload, and the estimated user's behavior, the workload corresponding to the user's behavior during the travel route is calculated. Scheduling that performs a workload prediction process to be predicted and determines the timing to provide provision schedule information, which is information scheduled to be provided to the user when traveling along the travel route, based on the predicted workload. A process is performed, and the provision schedule information is output to the user at the timing.

  Also according to this navigation method, as in the above navigation device, the user's behavior when traveling on the scheduled travel route is estimated, the workload corresponding to the user's behavior is predicted, and the predicted workload is used. The timing for providing the provision schedule information can be determined. Thus, by predicting the future workload, it is possible to provide information to the user at an optimal timing with planning to the future.

  The navigation program of the present invention is a program for realizing a navigation function stored in a memory, wherein the memory includes travel route data indicating a travel route on which a vehicle driven by a user travels, and vehicle travel Workload correlation data indicating the correlation between the user's behavior and the workload, and provision schedule information which is information scheduled to be provided to the user when traveling on the travel route is stored, and a computer is stored. Based on the travel route data, user behavior estimation processing means for performing user behavior estimation processing for estimating the behavior of the user traveling on the travel route, based on the estimated user behavior and the workload correlation data , Workload prediction for predicting a workload corresponding to the user's behavior while traveling on the travel route Function as a workload prediction processing means for performing processing, and a scheduling processing means for performing scheduling processing for determining a timing for providing the provision schedule information to the user when traveling on the travel route based on the predicted workload Let

  Also according to this navigation program, as in the above navigation device, the user's behavior when traveling on the planned travel route is estimated, the workload corresponding to the user's behavior is predicted, and based on the predicted workload The timing for providing the provision schedule information can be determined. Thus, by predicting the future workload, it is possible to provide information to the user at an optimal timing with planning to the future.

  According to the present invention, the behavior of a user traveling on a travel route is estimated, the workload corresponding to the user's behavior is predicted, and the timing for providing the provision schedule information is determined based on the predicted workload. Thus, information can be provided to the user at an optimal timing with a planability to the future.

  Hereinafter, a navigation device, a method, and a program according to an embodiment of the present invention will be described with reference to the drawings. In the present embodiment, for example, a case where it is used as a car navigation device mounted on an automobile is illustrated. This car navigation apparatus has a navigation function that provides various information such as restaurant information, traffic jam information, and new DVD information in addition to route guidance information. This navigation function is realized by a program stored in the memory or HDD of the car navigation device.

(First embodiment)
A car navigation device according to a first embodiment of the present invention will be described. Here, first, an outline of a main configuration of the car navigation apparatus will be described. FIG. 1 is a block diagram showing the configuration of the car navigation apparatus. As shown in FIG. 1, a car navigation device 1 includes an input unit 2 such as a remote controller and a touch panel, a current position measurement unit 3 such as a GPS device, and a travel route calculation unit 4 that obtains a travel route from a departure place to a destination. It has.

  In addition, the car navigation device 1 includes a user behavior estimation processing unit 5 that estimates the behavior of a user (driver) traveling on the travel route, and a workload prediction processing unit 6 that predicts a workload corresponding to the user's behavior. A scheduling processing unit 7 is provided for determining timing for providing information to a user traveling on the travel route. Here, the user behavior estimation processing unit 5 corresponds to the user behavior estimation processing unit of the present invention, and the workload prediction processing unit 6 corresponds to the workload prediction processing unit of the present invention. The scheduling processing unit 7 corresponds to the scheduling processing means of the present invention.

  Furthermore, the car navigation apparatus 1 includes an information selection unit 8 that selects information to be provided, and an information output unit 9 such as a liquid crystal screen and a speaker for outputting information to the user. In addition, the car navigation device 1 includes an event detection unit 10 that detects that a predetermined event has occurred while traveling along the travel route. Here, the information selection unit 8 corresponds to the information selection unit of the present invention, and the information output unit 9 corresponds to the information output unit of the present invention. Moreover, the event detection part 10 is equivalent to the event detection means of this invention.

  As shown in FIG. 1, the car navigation device 1 stores a travel route data storage unit 11 in which travel route data is stored, and workload correlation data indicating a correlation between user behavior and workload. The correlation data storage unit 12 is provided. Here, the travel route data storage unit 11 corresponds to the travel route data storage unit of the present invention, and the correlation data storage unit 12 corresponds to the correlation data storage unit of the present invention.

  In addition, the car navigation device 1 includes an action characteristic data storage unit 13 in which action characteristic data indicating a user's action characteristic while the vehicle is traveling and an interest characteristic data storage in which interest characteristic data indicating the user's interest characteristic is stored. Part 14 is provided. Here, the behavior characteristic data storage unit 13 corresponds to the behavior characteristic data storage unit of the present invention, and the interest characteristic data storage unit 14 corresponds to the interest characteristic data storage unit of the present invention.

  Further, the car navigation device 1 includes an all-registration information storage unit 15 in which a lot of various types of information (all-registration information) is stored, and information selected as information scheduled to be provided to the user from among a lot of information A provision schedule information storage unit 16 in which (provision schedule information) is stored is provided. Here, the provision schedule information storage unit 16 corresponds to provision schedule information storage means of the present invention.

  Next, each configuration of the car navigation device 1 will be described in detail. FIG. 2 and FIG. 3A are explanatory diagrams of travel route data stored in the travel route data storage unit 11. When the user inputs information on the departure point and the destination from the input unit 2, a travel route is obtained by the travel route calculation unit 4. In this case, information on the current position of the vehicle may be obtained by the current position measurement unit 3 and automatically set as the departure place. The travel route data indicating the travel route thus obtained is stored in the travel route data storage unit 11.

  For example, in FIG. 2, exit the departure point, go straight on road A, turn left at intersection A, go straight on road B, turn right on intersection B, go straight on road C, turn left on intersection C, A travel route is shown in which the vehicle travels straight on road D, turns right at intersection D, and arrives at the destination.

  FIG. 4 is an explanatory diagram of the workload correlation data stored in the correlation data storage unit 12. In the present embodiment, driving workload correlation data is stored as the workload correlation data. The driving workload correlation data indicates the correlation between the driving operation when the user drives the vehicle and the driving workload resulting from the driving operation. The driving workload correlation data can be obtained by conducting a test in advance for measuring the correlation between the driving operation and the driving workload. Here, the driving workload refers to a workload required for basic driving operation.

  For example, in the example of the driving workload correlation data shown in FIG. 4, the driving workload of the driving operation “straight ahead” is “low”, and the driving workload of the driving operation “lane change” is “high”. is there. In addition, the driving workload of the driving operation “waiting for signal” is “low”. Furthermore, the driving workload for the driving operation “waiting for a left turn” is “low”, and the driving workload for the driving operation “turn left” is “medium”. In addition, the driving workload for the driving operation “waiting for right turn” is “medium”, and the driving workload for the driving operation “turn right” is “high”.

  FIG. 5 is an explanatory diagram of the behavior characteristic data stored in the behavior characteristic data storage unit 13. In the present embodiment, driving skill data indicating a user's driving skill is stored as behavior characteristic data. For example, in the example of the driving skill data shown in FIG. 5, it is indicated that the user is “good at going straight” and “not good at changing lanes”. Further, it is indicated that this user is “good at turning left” and “good at turning right”. Furthermore, it is shown that this user is “not good at driving on narrow roads”, “good at driving on wide roads”, and “not good at driving on highways”. Such driving skill data can be created and updated by learning from the user's travel history.

  FIG. 6 is an explanatory diagram of the interest characteristic data stored in the interest characteristic data storage unit 14. For example, the example of the interest characteristic data shown in FIG. 6 indicates that this user is “interested in traffic jam information”, “not interested in gas station information”, and “not interested in parking lot information”. Has been. Further, it is indicated that the user is “interested in music information”, “interested in restaurant information”, and “not interested in leisure facility information”.

  FIG. 7 is an explanatory diagram illustrating an example of all registration information stored in the all registration information storage unit 15 and provision schedule information stored in the provision schedule information storage unit 16. As shown in FIG. 7, all registration information storage unit 15 stores information such as “restaurant information”, “traffic jam information”, “new DVD information”, “gas station information”, “parking lot information”, “leisure facility information”, etc. Information) is stored.

  Based on the travel route data and the interest characteristic data, the information selection unit 8 selects information (provision plan information) to be provided to the user traveling on the travel route from among all the registered information. The provision schedule information selected in this way is stored in the provision schedule information storage unit 16. When the information selection unit 8 selects provision schedule information from all registered information, a prediction algorithm such as a Bayesian network is used.

  For example, in FIG. 7, “congestion information” of a road in the travel route is selected as provision schedule information, reflecting the user's interest characteristic of “interested in congestion information”. Also, “new DVD information” is selected as information to be provided near the CD shop in the travel route, reflecting the user's interest characteristic of “interesting in music information”. Furthermore, “restaurant information” in the travel route is selected as provision schedule information reflecting the user's interest characteristic of “interested in restaurant information”.

  In the present embodiment, all the registration information and provision schedule information described above have constraint conditions. The constraint conditions include a time constraint, a location constraint, a sequence constraint, a clustering constraint, a priority constraint, a time constraint, a workload amount constraint, and the like.

  Here, the time-related constraint condition refers to a constraint condition for providing information from what time to what time, and the place-related constraint condition refers to a constraint condition for providing information from where to where. Further, the order-related constraint condition is a constraint condition that which information is provided before which information. The constraint condition related to clustering refers to a constraint condition that these pieces of information are provided together. The constraint condition regarding priority refers to a constraint condition that which information has higher priority than which information. Furthermore, the constraint condition related to the time width refers to a constraint condition that, for example, it takes “30 seconds” to provide information, and the constraint condition related to the workload amount is provided when the workload is “low”, for example. A constraint condition.

  FIG. 8 is an explanatory diagram showing an example of constraint conditions for all registration information and provision schedule information. In the example illustrated in FIG. 8, the restaurant information includes a restriction condition regarding a place “provide before the right turn of the intersection B”, a restriction condition regarding a time width “it takes 10 seconds to provide information”, and “ There is a constraint on the workload amount that "provide when the workload is low". In addition, the congestion information includes a restriction condition regarding a time “provide promptly after reception”, a restriction condition regarding a time width “it takes 30 seconds to provide information”, and “when the workload is low”. It has a constraint on the workload amount of “provide”. Furthermore, the new DVD information includes a restriction condition regarding a place “providing near a DVD store”, a restriction condition relating to a time width “it takes 10 seconds to provide information”, and “the workload is“ low ”. It has a constraint on the workload amount, which is sometimes provided. These constraints can be adapted by the Bayesian network based on learning from the user's profile, driving history, operation history, and the like.

  The operation of the car navigation apparatus 1 configured as described above will be described with reference to FIG. FIG. 9 is an explanatory diagram of user behavior estimation processing, workload prediction processing, and scheduling processing.

  The user behavior estimation processing unit 5 estimates a user's driving operation when traveling on the travel route based on the travel route data. In the present embodiment, as shown in FIGS. 2 and 3 (b), user behavior estimation processing is performed for a partial travel route that is a part of the travel route. For example, in FIG. 2 and FIG. 3B, the user behavior estimation process is performed for the partial travel route from the current position of the road B to the midpoint of the road D. In this case, as shown in FIG. 9, driving operations such as “straight ahead”, “wait for right turn”, “turn right”, “turn left”, “straight forward”, “wait for signal”, and the like are estimated. When the user behavior estimation processing unit 5 performs the user behavior estimation processing, for example, a prediction algorithm such as a Bayesian network is used.

  Based on the user's driving operation estimated as described above and the driving workload correlation data in FIG. 4, the workload prediction processing unit 6 predicts the driving workload of the user when traveling on the partial travel route. For example, in the example shown in FIG. 9, the driving workload when “straight ahead” on the road B is “low”, and the driving workload when “waiting for a right turn” before the intersection C is “ The driving workload when making a “right” at intersection C is “high”.

  In the present embodiment, the driving workload prediction can be corrected using the driving skill data. For example, in the example shown in FIG. 5, since the user is “good at turning right”, the driving workload when “waiting for right turn” before the intersection C is corrected from “medium” to “low”. The driving workload when making a “right turn” at intersection C can be corrected from “high” to “medium”.

  Further, based on the driving skill data “being a beginner”, a correction for increasing the base of the driving workload may be made. For example, it may be corrected to “medium” if the driving workload is predicted to be “low”, and may be corrected to “high” if it is predicted to be “medium”. In addition, correction that changes the base of the driving workload may be performed according to the day of the week (weekday / holiday) or the purpose of the drive (work / play). For example, the base of the driving workload is lowered on weekdays, and the base of the driving workload is raised on holidays. When the purpose of the drive is work, the base of the driving workload is raised, and when the purpose of the drive is play, the base of the driving workload is lowered.

  The scheduling processing unit 7 determines the timing for providing the provision schedule information to the user when traveling along the travel route based on the driving workload predicted as described above. For example, in the example illustrated in FIG. 9, when the road B is “straight ahead”, the scheduling process is performed so as to provide “restaurant information”. In this case, the operating workload is “low” and satisfies the constraint on the workload. Moreover, it is before turning right at the intersection B, and the constraints regarding the location are satisfied.

  In this scheduling process, the provision schedule information block is provided when the predicted driving workload is equal to or lower than a predetermined workload level “low” (that is, when the driving workload is “low”). On the other hand, when the predicted driving workload is higher than the predetermined workload level “low” (that is, when the driving workload is “medium” or “high”), the provision schedule information block is not provided.

  Here, it can be said that the provision schedule information is composed of provision schedule information blocks such as “restaurant information”, “congestion information”, and “new DVD information”. As shown in FIG. 9, the scheduling process is performed for each provision schedule information block so as to satisfy the constraint condition of the provision schedule information block. For example, a constraint condition related to the workload amount of the restaurant information will be described as an example. When the predicted driving workload is equal to or lower than the workload amount “low” of the constraint condition (that is, the driving workload is “low”). Restaurant information is provided. On the other hand, restaurant information is not provided when the predicted driving workload is higher than the constraint workload amount “low” (ie, when the driving workload is “medium” or “high”).

  As described above, the scheduling processing unit 7 determines the timing for providing each provision schedule information block so as to satisfy the constraint conditions of all the provision schedule information blocks. Determining the timing for providing each provision schedule information block so that all the constraint conditions of the provision schedule information block are satisfied is called solving a constraint satisfaction problem (Constraint Satisfaction Problems). In the present embodiment, for example, the above constraint satisfaction problem is solved using a backtracking method. In this case, the combination of forward checking can solve the constraint satisfaction problem more effectively.

  The event detection unit 10 detects whether or not a predetermined event has occurred while traveling on the travel route. In this embodiment, when leaving the departure place, when a predetermined time (for example, 5 minutes) has elapsed, when arriving at the end point of the partial travel route, when deviating from the travel route, when entering a traffic jam, etc. An event occurrence is detected. And when generation | occurrence | production of the above events is detected, a user action estimation process and a workload prediction process are performed again.

  Further, in the car navigation device 1 according to the present embodiment, it is possible to estimate the current workload and limit (stop) the provision of provision schedule information by using a biometric index or the like as the driving workload index. . As an index of the driving workload, for example, the movement of the user's line of sight and the pulse are used.

  FIG. 10 is a flowchart showing an operation flow of the car navigation apparatus 1. As shown in FIG. 10, in this embodiment, first, the event detection unit 10 determines whether or not an event has been detected (S1). When the occurrence of an event is detected, travel route data is acquired from the travel route data storage unit 11 (S2). On the other hand, if the occurrence of an event is not detected, the process ends.

  Next, driving skill data is acquired from the behavior characteristic data storage unit 13 (S3), and interest characteristic data is acquired from the interest characteristic data storage unit 14 (S4). Then, the information selection unit 8 performs an information selection process for selecting provision schedule information from all registered information (S5). Further, the operation workload correlation data is acquired from the correlation data storage unit 12 (S6).

  Then, the user behavior estimation processing unit 5 estimates the user's driving operation when traveling on the travel route based on the travel route data (S7), and the workload prediction processing unit 6 determines the estimated user driving operation and Based on the driving workload correlation data, the workload when traveling on the partial travel route is predicted (S8). Finally, based on the predicted driving workload, a scheduling process is performed to determine the timing for providing the provision schedule information to the user when traveling on the travel route (S9).

  According to the car navigation device 1 of the first embodiment as described above, the driving operation of the user traveling on the travel route is estimated, the driving workload corresponding to the driving operation of the user is predicted, and the predicted operation is performed. By determining the timing for providing the provision schedule information based on the manual workload, it is possible to provide information (provision schedule information) to the user at an optimal timing with a plan to the future.

  That is, in the present embodiment, a user's behavior when traveling on a scheduled travel route is estimated, a workload corresponding to the user's behavior is predicted, and provision schedule information is provided based on the predicted workload The timing to do can be determined. Specifically, the user's driving operation when traveling on the planned driving route is estimated, the driving workload corresponding to the user's driving operation is predicted, and the provision schedule information is obtained based on the predicted driving workload. The timing to provide can be determined. Thus, by predicting the future workload, it is possible to provide information to the user at an optimal timing with planning to the future.

  Moreover, in this Embodiment, a user's driving | operation workload when driving | running | working a driving | running route can be estimated in consideration of a user's driving skill. Therefore, the accuracy when predicting the driving workload is improved.

  Moreover, in this Embodiment, the provision schedule information provided to a user when driving | running a driving | running route can be selected in consideration of a user's interest characteristic. That is, only information of interest to the user (information that the user needs) is selected and provided to the user. Further, by selecting the provision schedule information in this way, it is possible to reduce the information amount of the provision schedule information handled in the scheduling process, and it is possible to reduce the time required for the scheduling process.

  In the present embodiment, the user behavior estimation process and the workload prediction process are performed only for the partial travel route that is a part of the travel route. Therefore, the time required for these processes can be shortened as compared to the case where the user behavior estimation process and the workload prediction process are performed for the entire travel route.

  Further, in the present embodiment, when a predetermined event occurs while traveling on the travel route, the user behavior estimation process and the workload prediction process are performed again. For example, the user behavior estimation process and the workload prediction process are performed again when the vehicle driven by the user passes an intermediate point (the end point of the partial travel route) or when the vehicle driven by the user deviates from the travel route. . As described above, even when a predetermined event occurs while traveling on the travel route, the user behavior estimation process and the workload prediction process are performed again to determine (change) the timing for providing the provision schedule information. Can do.

  Further, in the present embodiment, the provision schedule information can be provided to the user at an optimum timing in consideration of the predicted driving workload. For example, when the user performs a driving operation that requires attention (for example, turning right or changing lanes), the predicted driving workload of the user is high. At such timing, provision schedule information is not provided to the user. On the other hand, when a user performs a driving operation that requires less attention (for example, going straight ahead or waiting for a signal), the predicted driving workload of the user is low. The provision schedule information is provided to the user at such timing. In this way, provision schedule information is provided to the user at an optimal timing.

  Further, in the present embodiment, the provision schedule information can be provided to the user at an optimum timing in consideration of the workload amount of the constraint condition for each provision schedule information block. When the workload amount of the provision schedule information block is higher than the predicted user workload, the provision schedule information block is not provided to the user. On the other hand, when the workload amount of the provision schedule information block is lower than the predicted user workload, the provision schedule information block is provided to the user. In this way, provision schedule information is provided to the user at an optimal timing.

(Second Embodiment)
Next, a car navigation device 1 according to a second embodiment of the present invention will be described. The configuration of the car navigation device 1 of the present embodiment is the same as that of the car navigation device 1 of the first embodiment. That is, in the present embodiment, data used for processing is different. Specifically, the workload correlation data stored in the correlation data storage unit 12 and the behavior characteristic data stored in the behavior characteristic data storage unit 13 are different from those in the first embodiment. Hereinafter, these differences will be mainly described. Unless otherwise stated here, the configuration and operation are the same as those in the first embodiment.

  In the correlation data storage unit 12, as the workload correlation data, in addition to the operation workload correlation data described in the first embodiment, the device operation workload correlation data, the visual workload correlation data Auditory workload correlation data and psychogenic workload correlation data are stored.

  The device operation workload correlation data indicates the correlation between the device operation when the user operates the device provided in the vehicle and the device operation workload resulting from the device operation. The equipment operation workload correlation data can be obtained by performing a test in advance to measure the correlation between the equipment operation and the equipment operation workload. Here, the device operation workload refers to a workload required for device operation such as navigation operation and air conditioner operation.

  FIG. 11 is an explanatory diagram illustrating an example of device operation workload correlation data. In the example illustrated in FIG. 11, the device operation workload for device operation “navigation operation” is “medium”, and the device operation workload for device operation “air conditioner operation” is “low”. In addition, the device operation workload of device operations of “radio operation”, “CD player operation”, and “television operation” is “medium”.

  The visual workload correlation data indicates a correlation between a visual action that the user uses vision and a visual workload caused by the visual action. This visual workload correlation data can be obtained by pre-testing to measure the correlation between visual behavior and visual workload. Here, the visual workload means a workload required when looking at an object outside the vehicle, a navigation screen, or the like.

  FIG. 12 is an explanatory diagram of an example of visual workload correlation data. In the example shown in FIG. 12, the visual workload of the visual action of “gazing at the screen” is “high”, and the visual workload of the visual action of “finding an object” is also “high”. is there. Further, the visual workload of the visual action of “looking straight ahead” is “low”, and the visual workload of the visual action of “checking the surroundings” is “medium”.

  The auditory workload correlation data indicates a correlation between an auditory motion in which the user uses hearing and an auditory workload caused by the auditory motion. This auditory workload correlation data can be obtained by performing in advance a test for measuring the correlation between the auditory movement and the auditory workload. Here, the auditory workload is a workload required when listening to sounds such as news and music.

  FIG. 13 is an explanatory diagram showing an example of auditory workload correlation data. In the example shown in FIG. 13, the auditory workload of the auditory action “listening to news” is “high”, and the auditory workload of the auditory action “listening to music” is “low”. Also, the auditory workload of the auditory movement “listening to the sports broadcast” is “medium”.

  The psychological workload correlation data indicates the correlation between the situation when the user drives the vehicle and the psychological workload caused by the situation. This psychological workload correlation data can be obtained by conducting a test in advance to measure the correlation between the situation when driving the vehicle and the psychological workload. Here, the psychological workload is a workload required for psychological factors such as fatigue and excitement.

  FIG. 14 is an explanatory diagram of an example of psychological workload correlation data. In the example shown in FIG. 14, the psychological workload in the situation “traveling on a narrow road” is “medium”, and the psychological workload in the situation “traveling on a wide road” is “low”. In addition, the psychological workload in the situation of “highway driving” is “medium”. In addition, the psychological workload in the situation of “daytime driving” is “low”, and the psychological workload in the situation of “night driving” is “high”.

  In addition to the driving skill data described in the first embodiment, behavior characteristic data indicating the behavior characteristic of the user is stored in the behavior characteristic data storage unit 13 as behavior characteristic data.

  FIG. 15 is an explanatory diagram illustrating an example of behavior characteristic data stored in the behavior characteristic data storage unit 13. In the example shown in FIG. 15, it is indicated that this user has behavioral characteristics related to device operations such as “perform navigation operation when the vehicle is stopped” and “turn on radio when entering the highway”. It is also shown that this user has behavioral characteristics related to visual actions such as “gazing at the navigation screen when the vehicle is stopped” and “looking straight ahead when traveling straight”. Furthermore, it is shown that this user has behavioral characteristics of “listening to music on general roads” and “listening to news on highways”. Such behavior characteristic data can be created and updated by learning from the user's travel history.

  A characteristic operation of the car navigation device 1 of the present embodiment as described above will be described.

  The user behavior estimation processing unit 5 estimates the user behavior when traveling along the travel route based on the behavior characteristic data. If the case of waiting for a signal on a general road at night is described as an example, based on the behavior characteristic data of FIG. 15, it is estimated that the user performs “navigation operation” at this time. At this time, it is presumed that the user is “gazing at the navigation screen”. Furthermore, it is estimated that the user is “listening to music” at this time.

  The workload prediction processing unit 6 predicts the user workload based on the user behavior estimated as described above and each workload correlation data. In the above example, the driving workload when “waiting for signal” is “low”, but it is estimated that “navigation operation” is performed at this time, so the equipment operation workload is “medium”. is there. At this time, it is estimated that “gazing at the navigation screen”, so the visual workload is “high”. In addition, the auditory workload is “low” because it is estimated that this is “listening to music”. And since this example is night driving, the psychological workload is “high”.

  In the present embodiment, the workloads predicted in this way are added together to obtain an average workload. In this case, the average workload is “medium”. In addition to the present embodiment, a predetermined weighting process may be performed on each predicted workload to obtain a weighted average workload. Moreover, it is good also considering the length of the vector which uses each workload as a component, and vector itself as a comprehensive workload.

  The scheduling processing unit 7 determines the timing for providing the provision schedule information to the user when traveling along the travel route based on the average workload predicted as described above. In addition to the present embodiment, the scheduling process may be performed separately for each workload (each element). For example, it is possible to perform a scheduling process of “providing only information using vision when the visual workload is low”.

  The car navigation device 1 according to the second embodiment can provide the same operational effects as those of the first embodiment.

  In addition, in the present embodiment, the user's device operation when traveling on the planned travel route is estimated, the device operation workload corresponding to the user's device operation is predicted, and the predicted device operation workload is obtained. Based on this, it is possible to determine the timing for providing the provision schedule information. Thus, by predicting the future equipment operation workload, it is possible to provide information to the user at an optimal timing with a plan to the future.

  Further, in the present embodiment, the estimated visual workload is estimated by estimating the visual motion of the user when traveling on the planned travel route, and predicting the visual workload corresponding to the visual motion of the user. The provision timing of provision schedule information can be determined based on the above. Thus, by predicting the future visual workload, it is possible to provide information to the user at the optimal timing with the planability to the future.

  Further, in the present embodiment, the user's auditory motion when traveling on the planned travel route is estimated, the auditory workload corresponding to the user's auditory motion is predicted, and the predicted auditory workload is predicted. The provision timing of provision schedule information can be determined based on the above. In this way, by predicting the future auditory workload, information can be provided to the user at the optimal timing with planning to the future.

  Further, in the present embodiment, the user's situation when traveling on the planned travel route is estimated, the psychological workload corresponding to the user's situation is predicted, and based on the predicted psychological workload The timing for providing the provision schedule information can be determined. Thus, by predicting a future psychological workload, information can be provided to the user at an optimal timing with a planability to the future.

  Moreover, in this Embodiment, not only a user's driving skill but a user's action characteristic can be considered and the action of the user who is drive | working a driving | running route can be estimated. Therefore, the accuracy when estimating the user's behavior is improved.

  The embodiments of the present invention have been described above by way of example, but the scope of the present invention is not limited to these embodiments, and can be changed or modified according to the purpose within the scope of the claims. is there.

  For example, “high / medium / low” of each workload (FIGS. 4, 11, 12, 13, and 14), driving skill data (FIG. 5), and “○ ××” of interest characteristic data (FIG. 6). “High / Medium / Low” in the constraint on the size of the workload that determines whether or not to provide information (FIG. 8) is not limited to a discrete value but may be a continuous value.

  As described above, the navigation device according to the present invention has an effect that information can be provided to the user at an optimal timing with planning to the future, and is useful as a navigation device for vehicles.

1 is a block diagram of a car navigation device in a first embodiment. It is explanatory drawing of the driving | running route data in 1st Embodiment. (A) It is explanatory drawing of the driving | running route in 1st Embodiment. (B) It is explanatory drawing of the partial travel path | route in 1st Embodiment. It is explanatory drawing of the driving | operation workload correlation data in 1st Embodiment. It is explanatory drawing of the driving skill data in 1st Embodiment. It is explanatory drawing of the interest characteristic data in 1st Embodiment. It is explanatory drawing of all the registration information and provision schedule information in 1st Embodiment. It is explanatory drawing of the restrictions of all the registration information (provision plan information) in 1st Embodiment. It is explanatory drawing of the user action estimation process, workload prediction process, and scheduling process in 1st Embodiment. It is a flowchart of operation | movement of the car navigation apparatus in 1st Embodiment. It is explanatory drawing of the apparatus operation workload correlation data in 2nd Embodiment. It is explanatory drawing of the visual workload correlation data in 2nd Embodiment. It is explanatory drawing of the auditory workload correlation data in 2nd Embodiment. It is explanatory drawing of the psychological workload correlation data in 2nd Embodiment. It is explanatory drawing of the action characteristic data in 2nd Embodiment.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Car navigation apparatus 5 User action estimation process part 6 Workload prediction process part 7 Scheduling process part 8 Information selection part 9 Information output part 10 Event detection part 11 Travel route data storage part 12 Correlation data storage part 13 Behavior characteristic data storage part 14 Interesting characteristic data storage unit 16 Provision schedule information storage unit

Claims (15)

Travel route data storage means for storing travel route data indicating a travel route on which a vehicle driven by the user is scheduled to travel;
Correlation data storage means for storing workload correlation data indicating a correlation between the user's behavior and the workload while the vehicle is running;
Provision schedule information storage means for storing provision schedule information which is information scheduled to be provided to the user when traveling on the travel route;
User behavior estimation processing means for performing user behavior estimation processing for estimating the behavior of the user traveling on the travel route based on the travel route data;
Based on the estimated user behavior and the workload correlation data, a workload prediction processing means for performing a workload prediction process for predicting a workload corresponding to the user behavior traveling on the travel route;
Scheduling processing means for performing a scheduling process for determining a timing for providing the provision schedule information to the user when traveling on the travel route based on the predicted workload;
Information output means for outputting the provision schedule information to the user at the timing;
A navigation device characterized by comprising: The workload correlation data is
Including driving workload correlation data indicating a correlation between a driving operation when the user drives the vehicle and a driving workload caused by the driving operation;
The user behavior estimation processing means includes
Based on the travel route data, the driving operation of the user traveling on the travel route is estimated,
The workload prediction processing means includes
Based on the estimated driving operation of the user and the driving workload correlation data, a driving workload corresponding to the driving operation of the user traveling on the travel route is predicted,
The scheduling processing means includes
The navigation device according to claim 1, wherein a timing for providing the provision schedule information to the user traveling on the travel route is determined based on the predicted driving workload. The workload correlation data is
Including device operation workload correlation data indicating a correlation between device operation when the user operates the device provided in the vehicle and device operation workload caused by the device operation;
The user behavior estimation processing means includes
Based on the travel route data, estimate the user operation of the user traveling on the travel route,
The workload prediction processing means includes
Based on the estimated device operation of the user and the device operation workload correlation data, the device operation workload corresponding to the user operation of the user traveling on the travel route is predicted,
The scheduling processing means includes
The navigation device according to claim 1, wherein a timing for providing the provision schedule information to the user traveling along the travel route is determined based on the predicted device operation workload. The workload correlation data is
Visual workload correlation data indicating a correlation between a visual action that the user uses vision and a visual workload caused by the visual action;
The user behavior estimation processing means includes
Based on the travel route data, estimating the user's visual movement while traveling the travel route,
The workload prediction processing means includes
Based on the estimated visual movement of the user and the visual workload correlation data, a visual workload corresponding to the visual movement of the user traveling along the travel route is predicted,
The scheduling processing means includes
The navigation device according to claim 1, wherein a timing for providing the provision schedule information to the user traveling on the travel route is determined based on the predicted visual workload. The workload correlation data is
Including auditory workload correlation data indicating a correlation between an auditory motion that the user uses hearing and an auditory workload caused by the auditory motion;
The user behavior estimation processing means includes
Based on the travel route data, the auditory movement of the user traveling on the travel route is estimated,
The workload prediction processing means includes
Based on the estimated auditory motion of the user and the auditory workload correlation data, predicting an auditory workload corresponding to the auditory motion of the user traveling along the travel route,
The scheduling processing means includes
The navigation apparatus according to claim 1, wherein a timing for providing the provision schedule information to the user traveling along the travel route is determined based on the predicted auditory workload. Travel route data storage means for storing travel route data indicating a travel route on which a vehicle driven by the user is scheduled to travel;
Correlation data storage means for storing psychological workload correlation data indicating a correlation between a situation when the user drives the vehicle and a psychological workload caused by the situation;
Provision schedule information storage means for storing provision schedule information which is information scheduled to be provided to the user when traveling on the travel route;
User behavior estimation processing means for performing user behavior estimation processing for estimating a situation when the user drives the vehicle on the travel route based on the travel route data;
Workload prediction processing for predicting a psychological workload corresponding to a situation when the user drives the vehicle on the travel route based on the estimated user situation and the psychological workload correlation data A workload prediction processing means for performing
Scheduling processing means for performing a scheduling process for determining a timing for providing the provision schedule information to the user traveling on the travel route based on the predicted psychological workload;
Information output means for outputting the provision schedule information to the user at the timing;
A navigation device characterized by comprising: Comprising behavior characteristic data storage means for storing behavior characteristic data indicating the behavior characteristic of the user while the vehicle is running;
The user behavior estimation processing means includes
The navigation device according to claim 1, wherein the behavior of the user traveling along the travel route is estimated based on the behavior characteristic data and the travel route data. The behavior characteristic data is
Including driving skill data indicating the driving skill of the user;
The workload prediction processing means includes
The navigation device according to claim 7, wherein the workload corresponding to the behavior of the user traveling on the travel route is predicted based on the driving skill data and the travel route data. Interest characteristic data storage means for storing interest characteristic data indicating the user's interest characteristic;
Information selection means for selecting the provision schedule information to be provided to the user when traveling on the travel route based on the interest characteristic data and the travel route data;
The navigation device according to claim 1, further comprising: The user behavior estimation processing means includes
For a partial travel route up to a midpoint of the travel route, estimate the user's behavior while traveling on the partial travel route,
The workload prediction processing means includes
The navigation apparatus according to claim 1, wherein a workload corresponding to an action of the user traveling on the partial travel route is predicted. Event detection means for detecting that a predetermined event has occurred during traveling on the travel route,
11. The user behavior estimation processing unit performs the user behavior estimation processing again when the event occurs, and the workload prediction processing unit performs the workload prediction processing again. The navigation device described. The scheduling processing means includes
When the predicted workload is below a predetermined workload level, the timing is determined so as to provide the provision schedule information, and when the predicted workload is higher than the workload level, the provision The navigation apparatus according to claim 1, wherein the timing is determined so as not to provide schedule information. The provision schedule information is composed of a plurality of provision schedule information blocks, and each of the provision schedule information blocks has constraint condition data relating to a workload amount,
The scheduling processing means includes
For each of the provision schedule information blocks, the scheduling process is performed, and when the predicted workload is equal to or less than the workload amount, the timing is determined so as to provide the provision schedule information block, and the predicted The navigation device according to claim 12, wherein when the workload is higher than the workload amount, the timing is determined so as not to provide the provision schedule information block. Based on travel route data indicating a travel route on which a vehicle driven by a user travels, user behavior estimation processing is performed to estimate the user's behavior while traveling on the travel route,
Based on the workload correlation data indicating the correlation between the user's behavior and the workload while the vehicle is traveling, and the estimated user's behavior, the workload corresponding to the user's behavior while traveling the travel route The workload prediction process that predicts
Based on the predicted workload, a scheduling process is performed to determine a timing for providing provision schedule information, which is information scheduled to be provided to the user when traveling on the travel route, to the user,
A navigation method, wherein the provision schedule information is output to the user at the timing. A program for realizing a navigation function stored in a memory,
In the memory,
When traveling on the travel route, travel route data indicating a travel route on which the vehicle driven by the user is traveling, workload correlation data indicating a correlation between the user's behavior and the workload while the vehicle is traveling, and Provision schedule information which is information scheduled to be provided to the user is stored in
Computer
User behavior estimation processing means for performing a user behavior estimation process for estimating the behavior of the user traveling on the travel route based on the travel route data;
Based on the estimated user behavior and the workload correlation data, a workload prediction processing means for performing a workload prediction process for predicting a workload corresponding to the user behavior traveling on the travel route,
Scheduling processing means for performing scheduling processing for determining a timing for providing the provision schedule information to the user when traveling on the travel route based on the predicted workload;
A navigation program characterized by functioning as

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