JP2011117905A - Route selection support device and route selection support method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To support the selection of a traveling route to keep an atmosphere in a cabin appropriate by using the tendency of a change in the goodness of the expression of an occupant in a vehicle. <P>SOLUTION: A detection means 740 receives the result of photographing by a photographing means 720, and detects the goodness of the expression of the occupant. The detection means 740 acquires a positioning result by a positioning means 710 and an identification result by an identification means 730, and the goodness of the expression of the occupant is related with the present position of a vehicle CR for the identified occupant, and then, the result is stored in a first storage means 750. A learning means 760 learns "the change in the goodness of expression" on the basis of information related to the goodness of expression. On the other hand, when supporting the selection of traveling route, a providing means 780 specifies the composition of the occupant according to the identification result by identification mean 730. The providing means 780 provides the selection support information on the traveling route on the basis of the learning result concerning the present position of the vehicle CR and the learning result of "the change in the goodness of expression". <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a route selection support device, a route selection support method, a route selection support program, and a recording medium on which the route selection support program is recorded.

  Conventionally, navigation devices mounted on vehicles have been widely used. In recent years, the technology related to navigation devices has been remarkably developed, and various technologies for improving user convenience have been proposed.

  As one of such proposed technologies, there is a technology in which a vehicle driver's body and face are photographed with a vehicle interior camera, and information relating to driving assistance is provided to the driver based on the photographing result (see Patent Document 1 below). , Called “conventional example”). In this conventional technique, the state of the driver's psychology that affects the driving operation during driving of the vehicle is estimated from the image taken by the vehicle interior camera, and the driving operation status and the driver for a predetermined period before and after alerting Are stored as alert conditions. When driving the vehicle, when the estimated driver's psychological state and the acquired driving operation situation coincide with the accumulated alert conditions, the driver is alerted. ing.

JP 2008-176510 A

  In the technology of the conventional example described above, the driver is photographed, the state of the driver's psychology that affects the driving operation at the time of driving the vehicle is estimated, and the estimation result is used for the driver. We are alerting you. Thus, the technology of this conventional example only alerts the driver, and does not provide support for selecting a travel route, which is one of the basic functions as a navigation device.

  By the way, the state of the driver's psychology when driving the vehicle may vary greatly depending on the travel route due to differences in the environment outside the vehicle, and this difference reflects the individuality of the driver. And if a driver | operator's psychological state is favorable, he can drive with a pleasant mood and leads also to safe driving | operation. In addition, a person other than the driver may ride on the vehicle, but the psychological state of the passenger may vary greatly depending on the travel route. And since the vehicle interior during driving | running | working is a closed room, the atmosphere in a vehicle interior largely depends on the passenger's psychological state. That is, if the passenger's psychological state is good, the atmosphere in the passenger compartment is also good, but if the passenger's psychological state is poor, the atmosphere is also poor.

  For this reason, there is a need for a technology that can support the selection of a preferable travel route for the passenger from a psychological aspect while reflecting the individuality of the driver and passengers (hereinafter also referred to as “passenger”). Meeting this requirement is one of the problems to be solved by the present invention.

  The present invention has been made in view of the above circumstances, and supports the selection of a travel route that maintains a good atmosphere in the passenger compartment by utilizing the tendency of changes in the degree of goodness of facial expressions of passengers in the vehicle. It is an object of the present invention to provide a new route selection support device and route selection support method that can be used.

  The invention according to claim 1 is a route selection support device that supports selection of a travel route of a vehicle, and includes a positioning unit that measures a current position of the vehicle; and an imaging unit that captures a face of a passenger of the vehicle. Detecting means for detecting a goodness of facial expression for each occupant based on a result of photographing by the photographing means; and a first memory for storing the detection result by the detecting means in association with the positioning result by the positioning means. Learning means for learning a change in the degree of goodness of the facial expression for each passenger on the route traveled based on the information stored in the first storage means; and based on the learning result by the learning means in the future Providing means for providing support information for supporting selection of the travel route of the route.

  According to a seventh aspect of the present invention, there is provided route selection, comprising: positioning means for measuring a current position of the vehicle; and photographing means for photographing a face of a passenger of the vehicle. A route selection support method used in a support device, wherein a detection step of detecting a goodness of facial expression for each occupant based on a photographing result by the photographing means; a positioning result by the positioning means; and the detection A learning step for learning a change in the degree of goodness of facial expression for each passenger on the route traveled based on the detection result in the step; and a support for selecting a future travel route based on the learning result in the learning step A route selection support method, comprising: a providing step of providing support information for the purpose.

  According to an eighth aspect of the present invention, there is provided a route selection support program that causes a calculation means to execute the route selection support method according to the seventh aspect.

  The invention described in claim 9 is a recording medium in which the route selection support program according to claim 8 is recorded so as to be readable by a calculation means.

It is a block diagram for demonstrating the structure of the route selection assistance apparatus which concerns on one Embodiment of this invention. It is a block diagram for demonstrating schematically the structure of the navigation apparatus which concerns on one Example of this invention. It is a figure which shows the example of the route for demonstrating the example of the log | history of the favorable degree of the expression for every passenger. FIG. 6 is a diagram (part 1) for explaining an example of a history of goodness of facial expressions when traveling on routes 1 and 2 shown in FIG. 3; FIG. 4 is a diagram (No. 2) for explaining an example of a history of the degree of goodness of facial expressions when traveling on routes 1 and 2 shown in FIG. 3. 3 is a flowchart for explaining a learning process of “change in goodness of facial expression” by the navigation device of FIG. 2. 3 is a flowchart for explaining a travel route selection support process by the navigation device of FIG. 2. It is a flowchart for demonstrating the selection assistance process of the driving | running route accompanying the route search in FIG. It is a flowchart for demonstrating the selection assistance process of the driving | running route which does not accompany a route search in FIG.

  Hereinafter, an embodiment of the present invention will be described with reference to FIG. In the following description, the same or equivalent elements are denoted by the same reference numerals, and redundant description is omitted.

[Constitution]
FIG. 1 shows a schematic configuration of a route selection support apparatus 700 according to an embodiment.

  As shown in FIG. 1, the route selection support device 700 is mounted on a vehicle CR and moves with the vehicle CR. The route selection support device 700 includes positioning means 710, imaging means 720, identification means 730, detection means 740, and first storage means 750. The route selection support apparatus 700 includes a learning unit 760, a second storage unit 770, and a providing unit 780.

  The positioning means 710 measures the current position of the vehicle CR. The positioning result by the positioning means 710 is sent to the detecting means 740 and the providing means 780.

  The photographing unit 720 includes a camera device, and photographs the face of the passenger of the vehicle CR under the control of the detecting unit 740. The result of photographing by the photographing means 720 is sent to the identifying means 730 and the detecting means 740.

  Here, the number of photographing means may be one as in the present embodiment, or may be the same as the number of vehicles CR. When the number of photographing means is one, the photographing field of view of the one photographing means is around the face of a passenger sitting in a predetermined seat (for example, a driver seat, a passenger seat, etc.). Alternatively, it may be the entire passenger compartment where the faces of all the passengers can be photographed. When the number of photographing means is the same as the number of vehicles CR, the photographing field of view of each photographing means can be around the face of each passenger seated in a different seat.

  The identification unit 730 receives a result of photographing by the photographing unit 720. And the identification means 730 identifies a passenger's individual based on the said imaging | photography result. The identification result by the identification unit 730 is sent to the detection unit 740 and the providing unit 780.

  The detection means 740 receives the result of photographing by the photographing means 720. And the detection means 740 detects the favorable degree of the expression for every passenger based on the said imaging | photography result. Here, for example, “goodness of facial expression” can be classified in stages such as “good”, “slightly good”, “normal”, “slightly bad”, “bad”, etc. .

  The detection means 740 receives the current position of the vehicle CR as a positioning result by the positioning means 710 and also receives the identification result of the individual passenger by the identification means 730. Then, the detecting means 740 associates the detected degree of goodness of the passenger's facial expression with the current position of the vehicle CR for each identified passenger.

  The first storage unit 750 has a nonvolatile storage area. The first storage unit 750 can be accessed by the detection unit 740 and the learning unit 760. In the storage area of the first storage unit 750, the detected degree of goodness of the detected passenger's facial expression is stored in association with the traveling position of the vehicle CR for each identified passenger.

  The learning unit 760 reads from the first storage unit 750 the degree of goodness of the passenger's facial expression associated with the travel position of the vehicle CR. Then, the learning unit 760 learns “change in the degree of goodness of facial expression” for each identified passenger on the traveled route based on the information stored in the first storage unit 750.

  As a result of such “learning of the change in the degree of goodness of facial expression on the route traveled”, for example, when the passenger P1 travels on different routes 1 and 2 from the same starting point A to the same destination B, In addition, while the route 1 travels as a whole, the expression is in a “good” state, while the route 2 travels as a whole, the facial expression becomes a “bad” state. On the contrary, the passenger P2 has a characteristic that the expression is “bad” as a whole while traveling on the route 1, but the expression is “good” as a whole while traveling on the route 2. Etc.

  Further, as a result of “learning the change in the degree of goodness of the facial expression in the traveled route”, for example, the passenger P3 is generally in the “good” state while traveling on the route 1, but the traveling on the route 2 In some cases, when the initial facial expression is “good”, the facial expression changes to “bad” later, and when the initial facial expression is “slightly poor”, the facial expression changes to “normal” later. There are examples.

  In addition, the learning unit 760 obtains the environment outside the vehicle during traveling, and considers the change in the environment outside the vehicle on the route traveled, and calculates the “change in the goodness of facial expression” for each identified passenger with respect to the change in the environment outside the vehicle. Further learning can be performed.

  Here, as the “environment outside the vehicle”, for example, “office district”, “amusement district”, “characteristic building (landmark)”, “factory zone”, “residential area”, “along the mountain”, “along the coast” Or the like. When the learning unit 760 acquires such “external environment”, the “external environment” is acquired from the second storage unit 770 in which information on the external environment at each position on the map that can be read by the learning unit 760 is stored. Can be.

  As a result of such “learning of the change in the degree of goodness of the expression with respect to the change in the environment outside the vehicle”, for example, the passenger P1 has a tendency that the expression becomes “good” when the vehicle CR is traveling along the “mountain” However, there is a feature that the facial expression tends to be “bad” when traveling in the “office district”. On the contrary, the passenger P2 tends to have a “good” facial expression when the vehicle CR is traveling in the “office town”, but the facial expression is “when traveling along the mountain”. For example, there is a tendency to be in a “bad” state.

  The second storage unit 770 has a nonvolatile storage area, similar to the first storage unit 750 described above. The learning unit 760 can access the second storage unit 770. The storage area of the second storage unit 770 stores information on the environment outside the vehicle at each position on the map described above.

  The providing means 780 receives the identification result of each individual passenger by the identification means 730 and also receives the current position of the vehicle CR that is the positioning result by the positioning means 710. Then, the providing unit 780 provides support information for supporting the selection of a future travel route corresponding to the traveling passenger configuration based on the identification result of the passenger and the learning result by the learning unit 760. provide. The method of providing the “support information” may be an audio output by an audio output unit (not shown) or an image display by an image display unit (not shown).

  In the present embodiment, the providing unit 780 includes a search unit 781. The search unit 781 in the providing unit 780 searches for a travel route to the destination in consideration of the learning result by the learning unit 760. For example, when the passenger is P1, when searching for the travel route from the departure point A to the destination B described above, the result of “learning the change in the degree of goodness of the facial expression in the traveled route” is considered. Thus, route 1 is provided as a travel route. Similarly, when searching for a travel route from the departure point A to the destination B when the passenger is P2, the search means 781 “learns the change in the degree of goodness of the facial expression in the traveled route”. In consideration of the result of “,” route 2 is provided as a travel route.

  Further, when searching for a travel route from the current position to the destination Z, the search means 781 determines that the extracted travel route is a route on which the vehicle CR has not traveled so far. Consider the results of “Learning changes in facial expression”. Then, when the occupant is P1, the search unit 781 provides support as a travel route that travels along the “mountainside” for a long time from the searched route. Further, when the occupant is P2, the search means 781 provides a support route that travels the “office town” for a long time from the searched route.

  The travel route thus provided and supported is displayed as an image by the image display means and also as a sound output by the sound output means.

  Further, the providing unit 780 described above extracts future travel route candidates based on the positioning result by the positioning unit 710 even when the travel route is not searched. The providing unit 780 provides support information based on the application result of the learning result by the learning unit 760 for each extracted travel route candidate.

  For example, when the vehicle CR is traveling on the route 2 with the passenger P3 on it, the providing unit 780 travels on the route 2 as it is when the initial facial expression is “good”, and the facial expression is “bad” later. Since there is a tendency to change to the state, route 1 is extracted as a future travel route candidate. The providing unit 780 provides support so as to change the route 1 to the travel route.

  When the passenger is P1, the providing means 780 considers the result of “learning the change in the goodness of the facial expression with respect to the change in the outside environment” from the extracted driving route candidates from the extracted driving route candidates. A route that travels along the “long road” may be provided as a travel route. Further, when the passenger is P2, a route that travels long in the “office town” from among the extracted travel route candidates may be supported and provided as a travel route.

[Operation]
A route selection support method executed in the route selection support device 700 configured as described above will be described.

<Learning process for changes in facial expression>
First, the learning process of “change in the degree of goodness of facial expression” for each passenger by the route selection support device 700 will be described.

  In this learning process, the photographing unit 720 photographs the face of the passenger of the vehicle CR under the control of the detecting unit 740. The result of photographing by the photographing means 720 is sent to the identifying means 730 and the detecting means 740. In parallel with the photographing process by the photographing unit 720, the positioning unit 710 performs a measurement process of the current position of the vehicle CR. The positioning result by the positioning means 710 is sent to the detecting means 740.

  The identification unit 730 that has received the photographing result by the photographing unit 720 identifies each individual passenger based on the photographing result. The identification result by the identification unit 730 is sent to the detection unit 740.

  First, the detection means 740 detects the degree of goodness of the passenger's facial expression based on the result of photographing by the photographing means 720. And the detection means 740 acquires the positioning result by the positioning means 710 and the identification result by the identification means 730, and for each identified passenger, the detected goodness of the facial expression of the passenger is determined as the current position of the vehicle CR. Associate. Information relating to the degree of facial expression for each passenger associated with the travel position of the vehicle CR is stored in the first storage unit 750.

  When the information related to the goodness of the passenger's facial expression is thus stored in the first storage means 750, the learning means 760 changes the goodness of the facial expression for each identified passenger on the route traveled based on the information. To learn. In addition, the learning unit 760 acquires the running environment outside the vehicle from the second storage unit 770, and further learns the change in the degree of goodness of the facial expression for each identified passenger with respect to the change in the outside environment. The “change in the goodness of facial expression” for each passenger learned in this way is used in the process of providing support information for selecting a travel route.

<Providing support information>
Next, support information providing processing for selecting a travel route by the route selection support device 700 will be described. This support information provision process is performed in parallel with the learning process described above.

  In the support information providing process, the providing unit 780 first receives the identification result from the identifying unit 730 and specifies the passenger configuration of the vehicle CR. Then, the providing unit 780 receives the current position of the vehicle CR that is the positioning result by the positioning unit 710. Subsequently, the providing unit 780 searches based on the learning result of the learning unit 760 based on the current position of the vehicle CR and the learning result of “change in goodness of facial expression” by the learning unit 760. As a result, a search result for the travel route to the destination and support information for assisting selection of a future travel route having the above-described contents when the travel route is not searched are provided. As a result, the support information is provided to the passenger of the vehicle CR in the form of an image, sound, or the like.

  As described above, in the present embodiment, the face of the passenger in the vehicle CR is photographed by the photographing unit 720 and the photographing result is sent to the identifying unit 730 and the detecting unit 740. The identification unit 730 identifies each individual passenger based on the photographing result, and sends the identification result to the detection unit 740. First, the detection means 740 detects the degree of goodness of the passenger's facial expression based on the result of photographing by the photographing means 720. And the detection means 740 acquires the positioning result by the positioning means 710 and the identification result by the identification means 730, and for each identified passenger, the detected goodness of the facial expression of the passenger is determined as the current position of the vehicle CR. The first storage unit 750 stores the information in association with each other. When the information related to the goodness of the facial expression of the passenger is stored in the first storage means 750 in this way, the learning means 760 determines, based on the information, the “goodness of facial expression” for each identified passenger on the route that has traveled. To learn "changes".

  In parallel with this learning process, the providing unit 780 first receives the identification result by the identifying unit 730 and specifies the passenger configuration of the vehicle CR. Then, the providing means 780 receives the current position of the vehicle CR from the positioning means 710, and based on the current position of the vehicle CR and the learning result of “change in goodness of facial expression” by the learning means 760, Provide support information to support the selection of travel routes.

  Therefore, according to the embodiment of the present invention, it is possible to support selection of a travel route that maintains a good atmosphere in the passenger compartment by using the tendency of the change in the degree of goodness of the facial expression of the passenger of the vehicle.

  Note that the route selection support apparatus 700 of the above-described embodiment includes a computer as a calculation unit, and each of the above-described units excluding the positioning unit 710, the imaging unit 720, the first storage unit 750, and the second storage unit 770. This function can be realized by executing a program. These programs may be acquired in a form recorded on a portable recording medium such as a CD-ROM or DVD, or may be acquired in a form distributed via a network such as the Internet. Can do.

  Hereinafter, an embodiment of the navigation device of the present invention will be described with reference to FIGS. In the following description and drawings, the same or equivalent elements are denoted by the same reference numerals, and redundant description is omitted.

  FIG. 2 shows a schematic configuration of a vehicle-mounted navigation device 100 having a function as a route selection support device according to an embodiment. The navigation device 100 is an aspect of the route selection support device 700 (see FIG. 1) of the above-described embodiment. This navigation device 100 is mounted on a vehicle CR and is connected to a vehicle speed sensor 290 mounted on the vehicle CR.

[Constitution]
As shown in FIG. 2, the navigation device 100 includes a control unit 110 and a storage unit 120 as a first storage unit 750 and a second storage unit 770. In addition, the navigation device 100 includes a sound output unit 130, a display unit 140, and an operation input unit 150. Furthermore, the navigation apparatus 100 includes a travel information acquisition unit 160, a GPS (Global Positioning System) receiving unit 170 as a positioning unit 710, and two camera units 180 ₁ and 180 ₂ as an imaging unit 720. .

  The control unit 110 performs overall control of the entire navigation device 100. The control unit 110 will be described later.

  The storage unit 120 includes a hard disk device that is a nonvolatile storage device. The storage unit 120 stores various data such as navigation information (NVI), facial expression history information (FHI), facial expression change feature information (FAI), and the like. The storage unit 120 can be accessed by the control unit 110.

  The navigation information (NVI) stores various data used for navigation such as map data, POI (Point Of Interests) data, and background data. These data are read by the control unit 110. In addition, POI data includes “office district”, “amusement district”, “characteristic building (landmark)”, “factory zone”, “residential district”, “mountainside”, “ Information such as “along the coast” is also included.

  In the facial expression history information (FHI), information related to the “goodness of facial expression” for each passenger is stored in association with the travel position of the vehicle CR. A specific example of the facial expression history information (FHI) will be described later.

  The facial expression change feature information (FAI) stores the facial expression change feature for each passenger learned from the facial expression history information (FHI). A specific example of this facial expression change feature information (FAI) will also be described later.

  The sound output unit 130 includes a speaker, and outputs sound corresponding to the sound data received from the control unit 110. Under the control of the control unit 110, the sound output unit 130 outputs guidance voice information such as future driving route selection support information, the traveling direction of the vehicle CR, the driving situation, and the traffic situation during the navigation process.

  The display unit 140 includes a display device such as a liquid crystal panel, and displays an image corresponding to display data received from the control unit 110. The display unit 140 displays future driving route selection support information, map information, route information, guidance information, and the like during navigation processing under the control of the control unit 110.

  The operation input unit 150 includes a key unit provided in the main body of the navigation device 100 and / or a remote input device including the key unit. Here, as a key part provided in the main body part, a touch panel provided in a display device of the display unit 140 can be used. In addition, it can replace with the structure which has a key part, or can also employ | adopt the structure input with a sound using a voice recognition technique in combination.

  When the user operates the operation input unit 150, setting of operation contents and operation commands of the navigation device 100 are performed. For example, the user uses the operation input unit 150 to set support information providing processing start / stop commands in consideration of facial expression history, destinations related to route search in navigation processing, and the like. Such input contents are sent from the operation input unit 150 to the control unit 110 as operation input data.

  The travel information acquisition unit 160 includes an acceleration sensor, an angular velocity sensor, and the like, and detects acceleration and angular velocity acting on the vehicle CR. The travel information acquisition unit 160 is used for data exchange between the navigation device 100 and the vehicle speed sensor 290 mounted on the vehicle CR, and acquires speed data that is a detection result of the vehicle speed sensor 290. Each data thus obtained is sent to the control unit 110 as travel data.

  The GPS receiving unit 170 described above calculates the current position of the vehicle CR based on reception results of radio waves from a plurality of GPS satellites. Further, the GPS receiving unit 170 measures the current time based on the date / time information transmitted from the GPS satellite. Information regarding these current position and current time is sent to the control unit 110 as GPS data.

Each of the camera units 180 ₁ and 180 ₂ includes a camera device. In this embodiment, the camera units 180 ₁ and 180 ₂ are arranged at the upper front position in the vehicle interior. The camera unit 180 ₁ captures the face of the “driver”, and the camera unit 180 ₂ captures the face of the “passenger” sitting on the passenger seat. The imaging results obtained by the camera units 180 ₁ and 180 ₂ are sent to the control unit 110 as image information.

  Next, the control unit 110 will be described. The control unit 110 includes a central processing unit (CPU) and its peripheral circuits. The various functions described above are realized by the control unit 110 executing various programs. These functions include functions as the identification unit 730, the detection unit 740, the learning unit 760, and the providing unit 780 in the above-described embodiment.

  The control unit 110 is configured to use navigation data (NVI), facial expression history information (FHI), and facial expression in the storage unit 120 based on the traveling data from the traveling information acquisition unit 160 and the GPS data from the GPS receiving unit 170. By referring to the change feature information (FAI) as appropriate, the navigation information is provided to the passenger. The navigation information providing process includes (a) a map display for displaying a map of a region designated by the passenger on the display device of the display unit 140, (b) where the vehicle CR is located on the map, , Which direction is calculated, and is displayed on the display device of the display unit 140 and displayed to the occupant, and (c) the display device of the display unit 140 and the speaker of the sound output unit 130 are used. Processing such as “providing support information for supporting selection of future travel routes” is included.

In order to perform the selection support of the travel route in the providing process (c) described above, the control unit 110 controls the camera units 180 ₁ and 180 _2, and the face of the “driver” that is a result of photographing by the camera unit 180 ₁ image information, and acquires the image information of the face of a photographing result obtained by the camera unit 180 ₂ "passenger". Then, the control unit 110 identifies each individual passenger from the image information of the face of the “driver” and the image information of the face of the “passenger”. Further, the control unit 110 determines the degree of goodness of the facial expression from the image information of the faces of the driver and passengers (hereinafter also referred to as “passengers”). In the present embodiment, “goodness of facial expression” is classified in stages such as “good”, “slightly good”, “normal”, “slightly bad”, “bad”, and the like.

  In the present embodiment, when determining the degree of goodness of facial expression, the control unit 110 first detects the position of the left and right eyes and the position of the mouth from the image information of the passenger's face. Then, the control unit 110 calculates the interocular distance and the inclination from the detection positions of the left and right eyes and the detection position of the mouth, and derives the face size and the face orientation. In addition, the control unit 110 extracts, as feature points, points on the top, bottom, left, and right of each eye, points on the left and right of each eyebrow, and points on the top, bottom, left, and right of the mouth. The control unit 110 comprehensively analyzes the variation of the feature points extracted in this way, and determines the goodness of the facial expression. For example, if it is analyzed from these feature points that the cheek is lifted and the edge of the lips is pulled up, the control unit 110 is that the passenger is smiling, that is, the “goodness of facial expression” is “good”. It is determined that

  Subsequently, the control unit 110 obtains the current position of the vehicle CR and associates the goodness of the facial expression with the current position of the vehicle CR for each identified passenger. Information relating to the degree of facial expression for each passenger thus associated with the travel position of the vehicle CR is stored in the storage unit 120 as facial expression history information (FHI).

  Here, an example of the history of “goodness of facial expression” for each passenger when the vehicle CR travels through each point shown in FIG. 3 is shown. It is assumed that the route passing through points A → C → D → E → B is “route 1” and the route travels along the “mountainside” for a long time. Further, a route passing through points A → F → G → H → B is set as “Route 2”, and “Office Town” is set as a route that travels long.

  FIG. 4A shows a history of the goodness of facial expressions of the driver DR and the passengers P1 and P2 when traveling on the route 1, and FIG. 4B shows a history when traveling on the route 2. The history of the goodness of the facial expressions of the driver DR and the passengers P1 and P2 is shown. FIGS. 5A and 5B show the history of the goodness of facial expressions of the driver DR and the passenger P3 when traveling on the route 1 and the route 2, respectively. The history of the degree of goodness of the facial expression of the driver DR is an average of the collected results so far. In this embodiment, the range of “goodness of facial expression” is −5 to 5, 5 is “good”, 3 is “slightly good”, 0 is “normal”, -3 is “slightly bad”, Then, −5 is classified as “defective” and is classified in stages.

  When the facial expression history information (FHI) is stored in the storage unit 120 in this way, the control unit 110 performs “change in the degree of goodness of facial expression” for each passenger on the route traveled based on the facial expression history information (FHI). To learn. In this learning, the control unit 110 acquires POI data of navigation information (NVI), and further learns “changes in the degree of goodness of facial expression” for each passenger with respect to changes in the environment outside the vehicle on the traveled route.

  For example, in the example shown in FIGS. 4A and 4B, the passenger P1 has a “good” facial expression while traveling on route 1, but has a “bad” facial expression while traveling on route 2. And the passenger P2 is said to have a “bad” facial expression while traveling on the route 1 but a “good” facial expression while traveling on the route 2. Features are learned. In addition, the driver DR learns that the facial expressions are both “normal” during traveling on the routes 1 and 2. Here, this learning is referred to as “travel route learning”.

  In addition, from the example shown in FIGS. 4A and 4B, the passenger P1 looks “good” when traveling along the “mountain”, but travels in the “office district”. While the passenger P2 learns that the facial expression is in a “bad” state, the passenger P2 is in the “good” state when driving in the “office district”. The characteristic that the facial expression becomes “bad” when the vehicle is running is learned. Here, this learning is referred to as “exterior environment learning”.

  Further, in the example shown in FIGS. 5A and 5B, the passenger P3 is in the “good” state while traveling on the route 1, but the initial facial expression while traveling on the route 2. When “Good” state, the facial expression changes to “Bad” state later, and when the initial facial expression is “Slightly bad” state, the facial expression changes to “Normal” state later. Such learning results are stored in the storage unit 120 as facial expression change feature information (FAI).

  In addition, the control unit 110 performs future travel route selection support based on the learning result of “change in the degree of goodness of facial expression” in order to perform travel route selection support in the providing process (c) described above. Provide support information. Processing related to the provision of the support information will be described later.

[Operation]
The operation of the navigation device 100 configured as described above will be described mainly focusing on support information providing processing for performing support for selecting a future travel route.

In the following, during the execution of the support information providing process, the control unit 110 acquires the image information of the passenger's face from the camera units 180 ₁ and 180 ₂ at a predetermined time interval, and the vehicle CR. It is assumed that the current position of is acquired.

<Learning process for “change in facial expression”>
First, the learning process of “change in the degree of goodness of facial expression” used for the support information providing process will be described.

This learning process is started when the user uses the operation input unit 150 to input an information providing process start command in consideration of the facial expression history. When the start command is input, as shown in FIG. 6, in step S < _b > 11, the control unit 110 detects the driver and passenger's face image information acquired from the camera units 180 ₁ and 180 _2. Identify individuals. Thereafter, the process proceeds to step S12.

  In step S12, the control unit 110 determines the degree of goodness of the facial expression from the image information of the driver's and passenger's faces. Subsequently, in step S13, the control unit 110 associates the determined degree of facial expression with the current position of the vehicle CR and stores it in the storage unit 120 as facial expression history information (FHI) for each identified passenger. Thereafter, the process proceeds to step S14.

  In step S14, the control unit 110 determines whether facial expression history information (FHI) has been accumulated to such an extent that a significant learning result can be obtained. If the result of this determination is negative (step S14: N), the process returns to step S12.

  On the other hand, when the result of the determination in step S14 is affirmative (step S14: Y), the process proceeds to step S15. In this step S15, the control unit 110 learns “change in the degree of goodness of the facial expression in the traveled route” for each passenger based on the facial expression history information (FHI) and the navigation information (NVI) (travel route learning). ) And learning of “change in the degree of goodness of facial expression with respect to changes in the environment outside the vehicle” (exterior environment learning) for each passenger. Thereafter, the process returns to step S12.

  Thereafter, the processes of steps S12 to S15 are repeated, and the learning process of “change in the degree of goodness of facial expression” is performed.

<Providing support information>
Next, a description will be given of support information provision processing for supporting selection of a future travel route to which the learning result of “change in the degree of goodness of facial expression” is applied. This support information providing process is performed in parallel with the learning process described above.

  In the following, a specific example will be described with reference to the history of the degree of goodness of facial expressions shown in FIGS. Here, from FIG. 4 and FIG. 5, since there is no feature in the facial expression history of the driver DR, as a specific example, a process for providing support information based on the facial expression history of the passengers P1 to P3 will be described. Further, it is assumed that passengers seated in the passenger seat are not changed until the support information providing process is completed.

The support information providing process in the navigation device 100 is started when the user uses the operation input unit 150 to input a start command for the information providing process considering the facial expression history, as in the learning process described above. . Thus the start command is input, as shown in FIG. 7, in step S21, the control unit 110, from the image information of the face of the obtained from the camera unit 180 ₂ "passenger", identify the individual occupant To do.

  Subsequently, in step S22, the control unit 110 determines whether or not a learning result of “change in goodness of facial expression” of the identified passenger is obtained. If the result of this determination is negative (step S22: N), the process of step S22 is repeated. Even if the result of this determination is negative, the learning process of “change in goodness of facial expression” described above is performed, and the learning result of “change in goodness of facial expression” of the identified passenger is obtained. If generated, the result of the determination is affirmative.

  On the other hand, if the result of the determination in step S22 is affirmative (step S22: Y), the process proceeds to step S23.

  In step S23, the control unit 110 determines whether to perform route search. If the result of this determination is affirmative (step S23: Y), the process proceeds to step S24. In step S24, "travel route selection support process with route search" is performed. On the other hand, when the result of the determination in step S23 is negative (step S23: N), the process proceeds to step S25. In this step S25, "travel route selection support process without route search" is performed.

  The “travel route selection support with route search” processing in step S24 will be described. In this travel route selection support process, as shown in FIG. 8, first, in step S31, the control unit 110 searches for a route from the current position to the destination designated by the user.

  Subsequently, in step S32, the control unit 110 evaluates each searched route based on the learning result. In performing such evaluation, the control unit 110 first determines whether or not there is an identified passenger facial expression history information (FHI) for each searched route. Then, the control unit 110 evaluates the travel route using the result of the “travel route learning” described above for the route determined to have facial expression history information (FHI).

  Further, the control unit 110 determines whether or not the above-described “out-of-vehicle environment learning” result can be used for performing the evaluation for a route determined to have no facial expression history information (FHI). Then, the control unit 110 evaluates the travel route using the result of “learning outside the vehicle” for the route determined to be able to use the learning result.

  Then, the control unit 110 is searched by comprehensively considering the evaluation result of the driving route using the result of “learning driving route” and the evaluation result of the driving route using the result of “learning outside the vehicle”. Comprehensive evaluation for each route.

  For example, if there is a highly evaluated route in the evaluation result of the travel route using the results of “travel route learning” and “exterior environment learning”, the comprehensive evaluation is performed using the result of “travel route learning”. A weight is placed on the evaluation of the travel route using the result of “travel route learning”, such as making the travel route used more highly evaluated.

  Here, a specific example of the evaluation result will be described. For example, when the searched routes are the above-described routes 1 and 2 (see FIGS. 3, 4A and 4B), and the identified passenger is the passenger P1, the control unit 110 Using the result of “travel route learning”, route 1 is set as a highly evaluated travel route and route 2 is set as a low evaluated travel route. When the identified passenger is the passenger P2, the control unit 110 uses the result of “travel route learning” to set the route 2 as a highly evaluated travel route and reduce the route 1 to a low value. This is the evaluation route.

  Further, for example, when the passenger in the passenger seat is the passenger P1, and there is no facial expression history information (FHI) for the searched route, the control unit 110 uses the result of “learning outside the vehicle”. If a route that travels along the “mountain” for a long time is found in the searched route, the route is regarded as a highly rated travel route, and if a route travels in the “office district”, the route is rated low. As the driving route. When the passenger in the passenger seat is the passenger P2, and there is no facial expression history information (FHI) for the searched route, the control unit 110 uses the result of “learning outside the vehicle” to search. If there is a route that travels in the “office district” for a long time, the route is regarded as a highly rated travel route, and if a route travels along the mountain, the route is regarded as a low rated travel route. Root.

  In this embodiment, when it is determined that all of the searched routes cannot use the result of “external environment learning”, or there is only a low evaluation route based on the evaluation result of the route based on the learning result. In this case, the route based on the priority (for example, time priority, distance priority, toll road priority, etc.) set by the passenger is provided according to the function of a conventional navigation device.

  When the evaluation result for each searched route is obtained, the process proceeds to step S33. In step S33, the control unit 110 provides travel route candidate information based on the application result of the learning result. This information provision is such that the driving route candidates are presented to the user in the order of high evaluation, and an image is displayed on the display device of the display unit 140 and voice guidance is provided by the speaker of the sound output unit 130.

  Subsequently, in step S34, when a route to be traveled is determined from the travel route candidates by the user, the control unit 110 guides the route. Thereafter, the process proceeds to step S35.

  In step S35, the control unit 110 determines whether the vehicle CR has reached the destination or has left the travel route. If the result of this determination is negative (step S35: N), the process of step S35 is repeated. On the other hand, when the result of the determination in step S35 becomes affirmative (step S35: Y), the process of step S24 ends, and the process returns to step S23 in FIG. 7 described above.

  Next, the “support for selecting a travel route without performing a route search” process in step S25 will be described. In this travel route selection support process, as shown in FIG. 9, first, in step S41, the control unit 110 extracts a route as a future travel route candidate based on the travel position of the vehicle CR.

  Subsequently, in step S42, as in step S32 described above, the control unit 110 evaluates each of the extracted routes as a result of “running route learning”, a result of “learning outside the vehicle environment”, or facial expression change feature information ( Based on FAI).

  Here, a specific example of the evaluation result will be described. For example, when the vehicle CR travels between points A → F, it passes through route 2, which is a route passing through points F → G → H → B, and points F → C → D → E → B. It is assumed that a route (here, “route 1c”) is extracted (see FIG. 3). At this time, the control unit 110 uses the result of the “travel route learning”, and when the passenger is the passenger P3 and the expression is in the “good” state, the control unit 110 travels the route 2 as it is and the expression becomes “bad” later. ”(See FIGS. 5A and 5B), the route 1c that travels from the point F → C and reaches the point B is set as a highly evaluated travel route.

  When the evaluation result for each extracted route is obtained, the process proceeds to step S43. In step S43, as in step S33 described above, the control unit 110 provides travel route candidate information based on the application result of the learning result. This information provision is such that the driving route candidates are presented to the user in the order of high evaluation, and an image is displayed on the display device of the display unit 140 and voice guidance is provided by the speaker of the sound output unit 130.

  Subsequently, in step S44, when a travel route estimated to travel in the future is narrowed down from the above-mentioned travel route candidates due to a change in the current position of the vehicle CR, the control unit 110 guides the narrowed travel route. I do. Thereafter, the process proceeds to step S45.

  In step S45, the control unit 110 determines whether or not the vehicle CR has left the narrowed travel route. If the result of this determination is negative (step S45: N), the processes of steps S44 and S45 are repeated. On the other hand, when the result of the determination in step S45 becomes affirmative (step S45: Y), the process of step S25 ends, and the process returns to step S23 in FIG. 7 described above.

  By executing the above processing, support information providing processing for supporting selection of a future travel route is performed.

As described above, in this embodiment, the control unit 110 acquires the image information of the passenger's face from the camera units 180 ₁ and 180 ₂ . Then, the control unit 110 identifies individual passengers from the face image information and determines the degree of goodness of the passenger's facial expression. Subsequently, for each identified passenger, the control unit 110 associates the goodness of the facial expression with the current position of the vehicle CR and stores it in the storage unit 120 as facial expression history information (FHI). Then, the control unit 110 learns “change in the degree of goodness of facial expression” for each passenger on the traveled route based on the facial expression history information (FHI).

  When the learning of “change in the degree of goodness of facial expression” proceeds in this way, the control unit 110 configures the occupant composition during traveling based on the current position of the vehicle CR and the learning result of the “change in goodness of facial expression”. In response to the above, support information for supporting selection of a future travel route is provided by an image, a sound, or the like.

  Therefore, according to the present embodiment, it is possible to support selection of a travel route that maintains a good atmosphere in the passenger compartment by using the tendency of the change in the degree of goodness of the facial expression of the passenger of the vehicle.

[Modification of Example]
The present invention is not limited to the above-described embodiments, and various modifications are possible.

  For example, in the above embodiment, the driving route selection support considering the change in the goodness of the facial expression of the passenger sitting in the passenger seat is performed, but the change in the goodness of the driver's facial expression is taken into account. You may be made to perform selection support of the run route.

  In the above embodiment, two camera units are provided. However, the number of camera units may be one or three or more and less than or equal to the vehicle capacity. Here, when the number of camera units is one, the field of view of the one camera unit may be around the face of the passenger sitting on the passenger seat, or the faces of all the passengers It may be the entire vehicle interior that can be photographed. When the number of camera units is the same as the number of vehicles, the shooting field of view of each camera unit may be around the face of each occupant seated in a different seat.

  In the above embodiment, the passenger is identified from the image information acquired from the camera unit. However, the passenger may not be identified. In this case, “change in the degree of goodness of facial expression” of the average passenger is learned, and based on the learning result, support information for supporting selection of a future travel route is provided.

  Further, in the above-described embodiment, when the learning result of “change in the degree of goodness of facial expression” is available, the selection of a future driving route is supported based on the learning result without considering other factors. did. On the other hand, in consideration of traffic jams, if there is a traffic route that is optimal from the viewpoint of maintaining a good atmosphere in the passenger compartment, a route that does not cause traffic jams should be provided. Also good.

  In addition, the determination process of the facial expression goodness is not limited to the method of the above embodiment, and other methods may be adopted as long as the goodness of the facial expression can be determined systematically. It may be.

  In the above-described embodiment, the functions of the units other than the positioning unit, the photographing unit, and the first and second storage units are realized by executing the program by the computer. However, all or one of these units is realized. The unit may be configured by hardware using a dedicated LSI (Large Scale Integrated circuit) or the like.

100 ... Navigation device (route selection support device)
110 ... Control unit (identification means, detection means, learning means, providing means)
120: Storage unit (first storage means, second storage means)
170 ... GPS receiving unit (positioning means)
180 ₁ ... Camera unit (photographing means)
180 ₂ ... Camera unit (photographing means)
700 ... Route selection support device 710 ... Positioning means 720 ... Imaging means 730 ... Identification means 740 ... Detection means 750 ... First storage means 760 ... Learning means 770 ... Second storage means 780 ... Provision means 781 ... Search means

Claims (9)

A route selection support device for supporting selection of a travel route of a vehicle,
Positioning means for measuring the current position of the vehicle;
Photographing means for photographing a face of a passenger of the vehicle;
Detecting means for detecting a goodness of facial expression for each passenger based on a result of photographing by the photographing means;
First storage means for storing the detection result by the detection means in association with the positioning result by the positioning means;
Learning means for learning a change in the degree of goodness of the facial expression for each passenger on the route traveled based on the information stored in the first storage means;
Providing means for providing support information for supporting selection of a future travel route based on a learning result by the learning means;
A route selection support apparatus comprising: An identification means for identifying an individual person of the passenger based on a result of photographing by the photographing means;
In the first storage means, for each passenger identified by the identification means, the detection result by the detection means is stored in association with the positioning result by the positioning means,
The learning means learns a change in the degree of goodness of the facial expression for each identified passenger on the traveled route based on the information stored in the first storage means,
The providing means provides the support information corresponding to a traveling passenger configuration obtained from the identification result by the identifying means.
The route selection support apparatus according to claim 1. The learning means acquires an environment outside the vehicle during traveling, and further learns a change in the degree of goodness of the facial expression for each passenger with respect to a change in the environment outside the vehicle in consideration of a change in the environment outside the vehicle on the route traveled.
The route selection support apparatus according to claim 1 or 2, characterized in that   4. The route selection support apparatus according to claim 3, further comprising second storage means in which information on the environment outside the vehicle at each position on the map readable by the learning means is stored.   The route selection according to any one of claims 1 to 4, wherein the providing unit includes a search unit that searches for a travel route to a destination in consideration of a learning result obtained by the learning unit. Support device. The providing means includes
Based on the positioning results by the positioning means, extract future driving route candidates,
Providing the support information based on the application result of the learning result by the learning means for each extracted travel route candidate;
The route selection support device according to any one of claims 1 to 5, wherein Route selection support used in a route selection support device for supporting selection of a travel route of the vehicle, comprising: positioning means for measuring a current position of the vehicle; and photographing means for photographing a face of a passenger of the vehicle. A method,
A detection step of detecting a goodness of facial expression for each passenger based on a result of photographing by the photographing means;
A learning step of learning a change in the degree of goodness of the facial expression for each occupant on the route traveled based on the positioning result by the positioning means and the detection result in the detection step;
A providing step of providing support information for supporting selection of a future travel route based on a learning result in the learning step;
A route selection support method comprising:   A route selection support program for causing a calculation means to execute the route selection support method according to claim 7.   9. A recording medium, wherein the route selection support program according to claim 8 is recorded so as to be readable by a calculation means.

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