JP6951178B2 - Control device and control system - Google Patents

Description

The present invention relates to a control device and a control system.

Conventionally, a navigation device that guides a route from the current position of a vehicle to a destination has been widely used. In the navigation device, for example, the driver selects a guidance target route to be guided from a plurality of route candidates, so that the guidance target route is guided. In addition, in order to assist the driver in selecting a route to be guided, a recommended route, which is a recommended route to be guided, is presented. Regarding the presentation of such a recommended route, a technique for realizing the presentation of a route that improves the comfort in the vehicle as a recommended route has been proposed.

For example, in Patent Document 1, when a search mode that prioritizes the ride quality of a vehicle is set in order to avoid a situation in which an occupant becomes unwell and cannot continue traveling the vehicle, a route candidate is traveled. By calculating the evaluation value of the ride quality of the vehicle and selecting the route to be guided based on the evaluation value calculated for each route candidate, the route that prioritizes the ride quality of the vehicle over efficiency and economy is selected. The technology to do is disclosed.

JP-A-2017-20859

By the way, in the technique for presenting a recommended route, it is considered desirable to present a route that further improves the comfort in the vehicle as a recommended route. For example, in the technique disclosed in Patent Document 1, a route that prioritizes the ride quality of the vehicle is specifically selected based on the acceleration received by the occupant when the vehicle travels on the route candidate. As described above, in the conventional technique, the possibility of food and drink scattered in the vehicle when traveling on the recommended route has not been considered. Therefore, it is expected that the comfort in the vehicle will be further improved by presenting the recommended route capable of suppressing the scattering of food and drink in the vehicle.

Therefore, the present invention has been made in view of the above problems, and an object of the present invention is to be able to realize the presentation of a recommended route capable of suppressing the scattering of food and drink in the vehicle. , To provide new and improved control devices and control systems.

In order to solve the above problems, according to a certain viewpoint of the present invention, the plurality of foods and drinks based on the food and drink information regarding the food and drink in the vehicle and the vehicle behavior prediction information regarding the behavior of the vehicle for each of the plurality of route candidates. A control device is provided, which comprises a determination unit for determining a recommended route from the route candidates of the above to the destination.

The determination unit relates to a specific unit that specifies a scattering index indicating the inherent easiness of scattering of the food or drink based on the food or drink information, and the vehicle behavior prediction information according to the scattering index. A setting unit for setting a threshold value and a route determination unit for determining the recommended route based on the comparison result between the vehicle behavior prediction information for each of the plurality of route candidates and the threshold value may be provided.

The specific unit may estimate the spillability of the food or drink based on the food or drink information, and specify the scattering index according to the estimation result of the spillability of the food or drink.

The specific unit may estimate the susceptibility of the food or drink to spill based on the container information regarding the food or drink container.

The specific part may estimate the spillability of the food or drink based on at least one of the type of the food and drink container or the size of the mouthpiece of the food and drink container.

The specific part may estimate the spillability of the food or drink based on at least one of the viscosity of the food and drink or the remaining amount of the food and drink.

The specific unit may estimate the ease of eating and drinking of the food and drink based on the food and drink information, and specify the scattering index according to the estimation result of the ease of eating and drinking of the food and drink.

The specific unit may estimate the ease of eating and drinking of the food and drink based on at least one of the temperature of the food and drink or the presence or absence of an auxiliary tool for eating and drinking the food and drink.

The setting unit may set the threshold value based on at least one of the clothes of the passenger in the vehicle, the number of passengers in the vehicle, the time zone to which the current time belongs, or the destination of the vehicle.

The determination unit may adjust the threshold value based on the relationship between the comparison result between the vehicle behavior prediction information and the threshold value and the actual behavior of the food and drink.

The route determination unit may determine the recommended route based on the number of points where the vehicle behavior prediction information for each of the plurality of route candidates exceeds the threshold value.

The route determination unit may determine the recommended route based on the magnitude of the vehicle behavior prediction information with respect to the threshold value at each point for each of the plurality of route candidates.

The control device may include a control unit for displaying the plurality of route candidates including the determined recommended route on the display device.

The control unit may display an object indicating a point where the vehicle behavior prediction information exceeds the threshold value in the plurality of route candidates including the recommended route on the display device.

The control unit may display the object on the display device by changing the display mode of the object according to the size of the vehicle behavior prediction information.

The control unit may display the object on the display device by making at least one of the dimensions, colors, or shapes of the object different according to the size of the vehicle behavior prediction information.

When there are a plurality of the foods and drinks in the vehicle, the determination unit may determine the recommended route by preferentially using the smaller threshold value among the threshold values corresponding to each of the plurality of foods and drinks.

When there are a plurality of the foods and drinks in the vehicle, the determination unit may determine the recommended route by preferentially using the threshold value corresponding to the foods and drinks eaten by the driver.

The control device may include a prediction unit that predicts a predicted value of acceleration generated in the vehicle as the vehicle behavior prediction information when traveling on each of the plurality of route candidates.

Further, in order to solve the above-mentioned problems, according to another viewpoint of the present invention, the detection device for detecting the food and drink information regarding the food and drink in the vehicle, and the above-mentioned food and drink information and each of the plurality of route candidates. A control system including a control device including a determination unit for determining a recommended route from the plurality of route candidates to a destination based on vehicle behavior prediction information regarding the behavior of the vehicle is provided.

As described above, according to the present invention, it is possible to realize the presentation of a recommended route capable of suppressing the scattering of food and drink in the vehicle.

It is a schematic diagram which shows an example of the schematic structure of the control system which concerns on embodiment of this invention. It is a block diagram which shows an example of the functional structure of the control device which concerns on this embodiment. It is a flowchart which shows an example of the flow of the process performed by the control device which concerns on this embodiment. It is explanatory drawing which shows an example of the image which the paper cup imaged by the upper camera is reflected. It is explanatory drawing which shows an example of the image which the paper cup imaged by the side camera is reflected. It is explanatory drawing which shows an example of the image which the can imaged by the upper camera. It is explanatory drawing which shows an example of another image which shows the can image | image taken by the upper camera. It is explanatory drawing which shows an example of the image which imaged by the upper camera and the paper cup which inserted the straw is reflected. It is explanatory drawing which shows an example of the image which imaged by the upper camera and the paper cup which attached the traveler lid is reflected. It is explanatory drawing which shows an example of the relationship between the estimation result of the spillability of a beverage, the estimation result of the ease of drinking of a beverage, and the index value. It is explanatory drawing which shows an example of the prediction result of the acceleration prediction value for each route candidate. It is explanatory drawing which shows an example of the database which the candidate of the acceleration prediction value is associated with each vehicle speed. It is explanatory drawing which shows an example of the correspondence relationship between a vehicle type and a reference database. It is explanatory drawing which shows an example of the correspondence relation between a vibration characteristic and a reference database. It is explanatory drawing which shows an example of the characteristic of the synthetic acceleration with respect to the combination of the international roughness index IRI and the vehicle speed for each vibration characteristic. It is a flowchart which shows an example of the flow of the 1st example of the process of determining a recommended route based on the comparison result of the acceleration prediction value and the threshold value performed by the control device which concerns on this embodiment. It is a flowchart which shows an example of the flow of the 2nd example of the process of determining a recommended route based on the comparison result of the acceleration prediction value and the threshold value performed by the control device which concerns on this embodiment. It is explanatory drawing which shows the display example of the route candidate when the 1st route is determined as a recommended route. It is explanatory drawing which shows the display example of the route candidate different from the example shown in FIG. 18 when the 1st route is determined as a recommended route. It is explanatory drawing which shows the display example of the route candidate when the 2nd route is decided as a recommended route.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the present specification and the drawings, components having substantially the same functional configuration are designated by the same reference numerals, so that duplicate description will be omitted.

<1. Control system configuration>
First, the configuration of the control system 1 according to the embodiment of the present invention will be described with reference to FIGS. 1 and 2.

FIG. 1 is a schematic diagram showing an example of a schematic configuration of the control system 1 according to the present embodiment. Specifically, FIG. 1 shows the inside of a vehicle (hereinafter, also referred to as own vehicle) on which the control system 1 is mounted, together with each component of the control system 1.

In the present specification, the traveling direction of the own vehicle is the forward direction, the opposite direction to the traveling direction is the rear direction, and the left and right sides in the state of facing the traveling direction are the left direction and the right direction, respectively, and the vertical upper side. And the vertical lower side will be described as an upward direction and a downward direction, respectively.

The control system 1, for example, includes a navigation device 20, a communication device 30, an upper camera 41, a side camera 42, a thermo camera 43, a weight sensor 44, and a vehicle speed sensor 45, as shown in FIG. , The acceleration sensor 46 and the control device 100 are provided. As described above, each of the above components included in the control system 1 is provided in the vehicle. In the control system 1, the upper camera 41, the side camera 42, the thermo camera 43, and the weight sensor 44 correspond to an example of a detection device that detects food and drink information regarding food and drink in the vehicle.

The navigation device 20 is a device that guides a route from the current position of the vehicle to the destination desired by the driver in response to an input operation by the driver. In addition, the navigation device 20 has a function of visually displaying information, and displays information related to route guidance. For example, the navigation device 20 displays the current position of the vehicle on the map, the guidance target route to be guided, the distance to the destination, the arrival time to the destination, and the like as information on the route guidance. The display by the navigation device 20 is controlled by the control device 100. The navigation device 20 corresponds to an example of the display device according to the present invention.

Further, the navigation device 20 can calculate the current position of the vehicle by receiving radio waves from a GPS (Global Positioning System) satellite or the like. Further, the navigation device 20 outputs the input information input by the driver to the control device 100. Specifically, the input information includes priority condition information indicating a priority condition that is prioritized as a condition of the guidance target route and destination information indicating a destination.

The information regarding the route guidance may be displayed by another display device having a function of visually displaying the information. In that case, the other display device may correspond to an example of the display device according to the present invention. For example, as another display device, a projector provided on the dashboard and projecting various images on the windshield may be used. The image projected from the projector reflects off the windshield and is visually recognized as a virtual image by the passenger. Such a projector can display various images by utilizing a technique called a head-up display (HUD). Further, for example, as another display device, a transmissive display provided on the windshield may be used.

The communication device 30 communicates with an external device outside the vehicle. Specifically, the communication device 30 communicates with an external device that stores information used in the processing performed by the control device 100. Further, the communication device 30 outputs the received information to the control device 100.

The upper camera 41 is a camera that acquires an image of the food and drink container as food and drink information by taking an image of the food and drink container in the vehicle from above. The image obtained by the upper camera 41 may be a visible light image or an infrared image. For example, the upper camera 41 is provided on the ceiling of the vehicle above the cup holder H10, and images the beverage container C10 housed in the cup holder H10 from above. The cup holder H10 is a recess in which the beverage container C10 is housed, and is provided, for example, between the driver's seat and the passenger seat. The beverage container C10 contains a beverage D10 as a food or drink.

Further, the upper camera 41 can output an image of the food and drink container seen from above obtained by imaging to the control device 100. Here, the upper camera 41 internally or externally installs an image processing device (not shown) that detects food and drink information such as the type of food and drink container reflected in the image by performing image processing on the image obtained by imaging. You may prepare. In that case, the upper camera 41 may output the food and drink information obtained by the image processing by the image processing device of the upper camera 41 to the control device 100.

The side camera 42 is a camera that acquires an image of the food and drink container as food and drink information by taking an image of the food and drink container in the vehicle from a direction intersecting the vertical direction. The image obtained by the side camera 42 may be a visible light image or an infrared image. For example, the side camera 42 is provided inside the cup holder H10 and images the beverage container C10 housed in the cup holder H10 from the side. A plurality of side cameras 42 may be provided at the inner portion of the cup holder H10 at intervals in the circumferential direction.

Further, the side camera 42 can output an image of the food and drink container viewed from a direction intersecting the vertical direction obtained by imaging to the control device 100. Here, the side camera 42 internally or externally installs an image processing device (not shown) that detects food and drink information such as the type of food and drink container reflected in the image by performing image processing on the image obtained by imaging. You may prepare for. In that case, the side camera 42 may output the food and drink information obtained by the image processing by the image processing device of the side camera 42 to the control device 100.

The thermo camera 43 is a camera that acquires an infrared image showing the temperature distribution of a region including a food and drink container used for detecting the temperature of food and drink in a vehicle as food and drink information by imaging. For example, the thermo camera 43 is provided on the ceiling of the vehicle above the cup holder H10, and images the beverage container C10 housed in the cup holder H10 from above.

Further, the thermo camera 43 can output an infrared image showing the temperature distribution of the region including the food and drink container obtained by imaging to the control device 100. Here, the thermo camera 43 internally or externally installs an image processing device (not shown) that detects the temperature of food and drink reflected in the infrared image as food and drink information by performing image processing on the infrared image obtained by imaging. You may prepare for. In that case, the thermo camera 43 may output the detection result of the temperature of food and drink by the image processing device of the thermo camera 43 to the control device 100. The image processing device of the thermo camera 43 can detect, for example, the average temperature or the maximum temperature in the region where the beverage container C10 is reflected in the infrared image as the temperature of the beverage D10.

In the above-mentioned image processing performed by the image processing device of each camera in the control system 1, for example, by using a technique such as machine learning, the area in which the object is reflected is extracted and the type of the object is recognized. Can be realized by. Specifically, the image processing device of each camera can extract a region in which an object is projected and recognize the type of the object by using a statistical model learned according to a well-known algorithm. The statistical model used for such image processing can be stored in, for example, a storage element of each image processing device.

The weight sensor 44 is a sensor that detects the weight of an object such as a beverage container C10 housed in the cup holder H10 as food and drink information. Further, the weight sensor 44 outputs the detection result to the control device 100.

The vehicle speed sensor 45 detects the vehicle speed, which is the speed of the vehicle. Further, the vehicle speed sensor 45 outputs the detection result to the control device 100.

The acceleration sensor 46 detects the acceleration generated in the vehicle. For example, the acceleration sensor 46 detects components of acceleration generated in the vehicle in the front-rear direction, the left-right direction, and the up-down direction, respectively. Further, the acceleration sensor 46 outputs the detection result to the control device 100.

Although FIG. 1 shows one cup holder H10 provided between the driver's seat and the passenger seat, the installation position and number of the cup holders H10 in the vehicle are not limited to such an example. For example, the cup holder H10 may be provided on the door side with respect to the seat. Further, for example, a cup holder H10 may be provided for each seat. In that case, the upper camera 41, the side camera 42, the thermo camera 43, and the weight sensor 44 may be provided at positions corresponding to the cup holders H10, respectively. The cup holder H10 may be removable from the vehicle.

The control device 100 stores a CPU (Central Processing Unit) which is an arithmetic processing unit, a ROM (Read Only Memory) which is a storage element for storing programs and arithmetic parameters used by the CPU, and parameters which are appropriately changed in the execution of the CPU. It is composed of a RAM (Random Access Memory) or the like, which is a storage element for temporary storage.

Further, the control device 100 communicates with each device in the control system 1. Communication between the control device 100 and each device is realized by using, for example, CAN (Control Area Network) communication. Specifically, the control device 100 communicates with the navigation device 20, the communication device 30, the upper camera 41, the side camera 42, the thermo camera 43, the weight sensor 44, the vehicle speed sensor 45, and the acceleration sensor 46. The function of the control device 100 according to the present embodiment may be divided by a plurality of control devices, and in that case, the plurality of control devices may be connected to each other via a communication bus such as CAN.

FIG. 2 is a block diagram showing an example of the functional configuration of the control device 100 according to the present embodiment.

The control device 100 includes, for example, an acquisition unit 110, a prediction unit 120, a determination unit 130, and a control unit 140.

The acquisition unit 110 acquires various information used in the processing performed by the control device 100. Further, the acquisition unit 110 outputs the acquired information to the prediction unit 120 and the determination unit 130.

For example, the acquisition unit 110 acquires information output from each device by communicating with each device in the control system 1.

Further, for example, the acquisition unit 110 may acquire information by further processing the information acquired from each device in the control system 1. Specifically, the acquisition unit 110 performs image processing similar to the above-mentioned image processing performed by the image processing device of each camera in the control system 1 on the image output from each camera, thereby performing the image processing of each camera. Information similar to the information obtained by the image processing device can be acquired. In that case, the statistical model used in the image processing may be stored in the storage element of the control device 100, or may be stored in the external device of the vehicle, for example.

The prediction unit 120 predicts the acceleration prediction value G, which is the prediction value of the acceleration generated in the vehicle when traveling on each route candidate, as the vehicle behavior prediction information. Further, the prediction unit 120 outputs the predicted acceleration prediction value G to the determination unit 130. As will be described later, the route candidate is searched by, for example, the route determination unit 133 of the determination unit 130 based on the input information from the driver.

The determination unit 130 determines a recommended route from a plurality of route candidates to the destination. Further, the determination unit 130 outputs information indicating a plurality of route candidates including the recommended route to the control unit 140. Specifically, the determination unit 130 determines from among the plurality of route candidates to the destination based on the food and drink information regarding the food and drink in the vehicle and the vehicle behavior prediction information regarding the vehicle behavior for each of the plurality of route candidates. The recommended route can be determined. The vehicle behavior prediction information is, in other words, information regarding the vehicle behavior predicted in the future. Further, the recommended route means a route recommended as a guide target route of the target guided by the navigation device 20.

For example, the determination unit 130 includes a specific unit 131, a setting unit 132, and a route determination unit 133.

The identification unit 131 specifies a scattering index indicating the unique easiness of scattering of food and drink at the time of eating and drinking based on the food and drink information.

For example, the specific unit 131 includes a first estimation unit 131a, a second estimation unit 131b, and an index value identification unit 131c.

The first estimation unit 131a estimates the susceptibility of food and drink to spillage based on food and drink information.

The second estimation unit 131b estimates the ease of eating and drinking of food and drink based on the food and drink information.

The index value specifying unit 131c specifies an index value as a scattering index according to at least one of the estimation results by the first estimation unit 131a or the second estimation unit 131b.

The setting unit 132 sets a threshold value for vehicle behavior prediction information according to the scattering index.

The route determination unit 133 searches for a plurality of route candidates based on the input information from the driver, and determines a recommended route from the plurality of route candidates. Specifically, the route determination unit 133 can determine the recommended route based on the comparison result between the vehicle behavior prediction information and the threshold value for each route candidate. The lane may be specified in a part or all of the route candidates to be searched, and the lane may be specified in a part or all of the recommended route to be determined. In that case, the navigation device 20 executes route guidance for each lane.

The control unit 140 controls the operation of the navigation device 20. Specifically, the control unit 140 controls the display of information related to route guidance by the navigation device 20. For example, the control unit 140 causes the navigation device 20 to display a plurality of route candidates including the determined recommended route. When the information related to the route guidance is displayed by another display device different from the navigation device 20, the control unit 140 causes the other display device to display a plurality of route candidates including the determined recommended route.

<2. Control device operation>
Subsequently, the operation of the control device 100 according to the present embodiment will be described with reference to FIGS. 3 to 20.

In the following, in order to facilitate understanding, an example in which the food / drink information used in determining the recommended route by the control device 100 is the food / drink information related to the beverage D10 will be mainly described, but the recommended route by the control device 100 will be mainly described. The food and drink information used in the determination may include food and drink information regarding food (specifically, food containing a relatively large amount of liquid such as food in which solid matter is immersed in liquid).

FIG. 3 is a flowchart showing an example of the flow of processing performed by the control device 100 according to the present embodiment. For example, the processing flow shown in FIG. 3 is constantly repeated.

When the processing flow shown in FIG. 3 is started, first, in step S501, the specific unit 131 determines whether or not the beverage container C10 is housed in the cup holder H10. If it is determined that the beverage container C10 is contained in the cup holder H10 (step S501 / YES), the process proceeds to step S503. On the other hand, when it is determined that the beverage container C10 is not contained in the cup holder H10 (step S501 / NO), the process proceeds to step S511.

For example, when the region in which the beverage container C10 is reflected is extracted from at least one of the images obtained by the upper camera 41 or the side camera 42, the specific unit 131 determines that the beverage container C10 is housed in the cup holder H10. obtain.

The specific unit 131 can determine whether or not an object is housed in the cup holder H10 based on the detection result of the weight sensor 44. Therefore, when it is determined that the cup holder H10 does not contain an object, the execution of the image processing for extracting the region in which the beverage container C10 is reflected may be omitted. As a result, the calculation load of the control device 100 can be reduced when the image processing for extracting the region in which the beverage container C10 is reflected can be performed by the acquisition unit 110.

In step S503, the first estimation unit 131a estimates the spillability of the beverage D10 based on the food and drink information related to the beverage D10.

For example, the first estimation unit 131a estimates the spillability of the beverage D10 based on the container information regarding the food and drink container. Specifically, the first estimation unit 131a estimates the spillability of the beverage D10 based on the type of the beverage container C10 or at least one of the widths of the drinking spouts of the beverage container C10. Further, for example, the first estimation unit 131a estimates the spillability of the beverage D10 based on at least one of the viscosity of the beverage D10 or the remaining amount of the beverage D10.

Specifically, the first estimation unit 131a first describes each parameter of the type of the beverage container C10, the width of the drinking port of the beverage container C10, the viscosity of the beverage D10, and the remaining amount of the beverage D10, and each parameter is the beverage D10. Calculate a score that indicates the degree of influence on the susceptibility to spillage. Then, the first estimation unit 131a calculates the total value of the scores for each parameter as the spillability score K indicating the spillability of the beverage D10. Then, the first estimation unit 131a estimates the spillability of the beverage D10 by comparing the spillability score K with the reference values K_h and K_l. Each score calculated in the process of estimating the spillability of the beverage D10 indicates, for example, that the larger the value, the easier the beverage D10 to spill. Further, the reference value K_h takes a larger value than the reference value K_l.

The first estimation unit 131a sets the score Y for the type of the beverage container C10 as, for example, "1" when the type of the beverage container C10 is a PET bottle or a bottle, and "2" when the type of the beverage container C10 is a paper pack. Calculate "3" when it is a can, "4" when it is a paper cup or plastic cup, and "0" when it is another (for example, a water bottle).

The type of beverage container C10 can be detected, for example, by image processing performed on at least one of the images obtained by the upper camera 41 or the side camera 42. In such image processing, specifically, a region in which the beverage container C10 is reflected can be extracted, and the type of the beverage container C10 can be detected based on the shape and dimensions of the region. The first estimation unit 131a may determine the type of the beverage container C10 based on the detection result of the weight sensor 44.

FIG. 4 is an explanatory view showing an example of the image Im11 in which the paper cup E11 captured by the upper camera 41 is projected. FIG. 5 is an explanatory diagram showing an example of an image Im12 in which the paper cup E11 captured by the side camera 42 is projected. For example, as shown in FIGS. 4 and 5, when the paper cup E11 is shown in the image Im11 obtained by the upper camera 41 or the image Im12 obtained by the side camera 42, the type of the beverage container C10 is a paper cup. It can be detected that there is. In that case, the first estimation unit 131a calculates "4" as the score Y for the type of the beverage container C10.

Further, in the first estimation unit 131a, as a score S regarding the width of the drinking spout of the beverage container C10, for example, when the ratio of the area of the drinking spout to the cross-sectional area of the large diameter portion of the beverage container C10 is equal to or more than the reference ratio. Is calculated as "2", and when it is smaller than the reference value, "1" is calculated. Specifically, the reference ratio is appropriately set to a value at which it can be determined whether or not the above ratio is large enough that the beverage D10 is expected to be relatively spilled. The information indicating the reference ratio may be stored in advance in the storage element of the control device 100, or may be stored in the external device of the vehicle.

The ratio of the area of the drinking spout to the cross-sectional area of the large diameter portion of the beverage container C10 can be detected, for example, by image processing performed on the image obtained by the upper camera 41. In such image processing, specifically, the area where the drinking spout is reflected is extracted by the binarization processing, and the above ratio is based on the shape and size of the area and the shape and size of the area where the beverage container C10 is reflected. Can be detected. The large diameter portion may be, for example, a portion having the largest outer diameter in the beverage container C10.

FIG. 6 is an explanatory view showing an example of an image Im13 in which the can E21 imaged by the upper camera 41 is projected. FIG. 7 is an explanatory view showing an example of another image Im14 in which the can E22 imaged by the upper camera 41 is shown. For example, a large-diameter crossing between a pull-top can E21 shown in image Im13 shown in FIG. 6 and a can E22 whose mouth is covered with a screw cap shown in image Im14 shown in FIG. The ratio of mouth area to area can vary. Specifically, when the cross-sectional area of the large diameter portion of each can is the same, the drinking spout E25 of the can E21 shown in FIG. 6 is smaller than the drinking spout E26 of the can E22 shown in FIG. Has an area. Therefore, the first estimation unit 131a calculates, for example, "1" in the example shown in FIG. 6 as the score S for the width of the drinking spout of the beverage container C10, while in the example shown in FIG. 7, "1" is calculated. 2 ”may be calculated.

Further, the first estimation unit 131a does not match "1" as the score M for the viscosity of the beverage D10, for example, when the type of the beverage D10 matches the type set as the beverage having a relatively high viscosity. In the case, "2" is calculated. Information indicating the type of beverage set as a relatively viscous beverage may be stored in advance in a storage element of the control device 100, or may be stored in an external device of the vehicle. The first estimation unit 131a calculates "2" as the score M when the type of the beverage D10 is not detected.

The type of beverage D10 can be detected, for example, by image processing performed on the image obtained by the side camera 42. In such image processing, specifically, the type of the beverage D10 can be detected by performing a process of recognizing the character string printed on the beverage container C10.

For example, as shown in FIG. 5, when the character string E12 printed on the paper cup E11 is reflected in the image Im12 obtained by the side camera 42, the type of the beverage D10 in the beverage container C10 can be detected. .. In that case, the first estimation unit 131a calculates the score M for the viscosity of the beverage D10 according to the detection result for the type of the beverage D10.

Further, the first estimation unit 131a sets the score C for the remaining amount of the beverage D10 to, for example, "2" when the remaining amount of the beverage D10 is half or more of the capacity of the beverage container C10, and the capacity of the beverage container C10. If it is less than half of, "1" is calculated. The first estimation unit 131a calculates "2" as the score C when the remaining amount of the beverage D10 is not detected.

The remaining amount of the beverage D10 can be detected, for example, by image processing performed on the image obtained by the upper camera 41. In such image processing, specifically, the region where the surface of the beverage D10 is reflected is extracted by the edge detection processing, and the beverage D10 is based on the relationship between the position of the region and the position of the region where the beverage container C10 is reflected. The remaining amount can be detected. The first estimation unit 131a may determine the remaining amount of the beverage D10 based on the detection result of the weight sensor 44.

For example, as shown in FIG. 4, when the edge of the surface of the beverage D10 in the paper cup E11 is reflected in the image Im11 obtained by the upper camera 41, the edge is detected by the edge detection process. Since the region where the surface of the beverage D10 is reflected can be extracted, the remaining amount of the beverage D10 can be detected.

The first estimation unit 131a calculates, for example, the total value of the score Y, the score S, the score M, and the score C as the spillability score K indicating the spillability of the beverage D10.

For example, when the spillability score K is larger than the reference value K_h, the first estimation unit 131a estimates the spillability of the beverage D10 as "easy to spill". Further, the first estimation unit 131a estimates that the spillability of the beverage D10 is "normal" when, for example, the spillability score K is equal to or less than the reference value K_h and is equal to or greater than the reference value K_l. Further, the first estimation unit 131a estimates that the spillability of the beverage D10 is "hard to spill" when the spillability score K is smaller than the reference value K_l. Specifically, the reference values K_h and K_l for estimating the spillability are appropriately set to values that can appropriately estimate the spillability of the beverage D10 according to the spillability score K calculated as described above. Will be done. The information indicating the reference values K_h and K_l may be stored in advance in the storage element of the control device 100, or may be stored in the external device of the vehicle.

Next, in step S505, the second estimation unit 131b estimates the ease of drinking of the beverage D10 based on the food and drink information related to the beverage D10.

For example, the second estimation unit 131b estimates the ease of drinking of the beverage D10 based on at least one of the temperature of the beverage D10 and the presence or absence of an auxiliary tool for eating and drinking of the beverage D10.

Specifically, the second estimation unit 131b first determines the ease of drinking of the beverage D10 with respect to each parameter of the temperature of the beverage D10, the presence / absence of a straw as an auxiliary tool, and the presence / absence of a traveler lid as an auxiliary tool. Calculate a score that indicates the degree of influence. Then, the second estimation unit 131b calculates the total value of the scores for each parameter as the drinkability score N indicating the drinkability of the beverage D10. Then, the second estimation unit 131b estimates the drinkability of the beverage D10 by comparing the drinkability score N with the reference values N_h and N_l. Each score calculated in the process of estimating the ease of drinking of the beverage D10 indicates, for example, that the larger the value, the harder it is to drink the beverage D10. Further, the reference value N_h takes a larger value than the reference value N_l.

The second estimation unit 131b calculates, for example, "2" when the temperature of the beverage D10 is equal to or higher than the reference temperature and "1" when the temperature of the beverage D10 is lower than the reference temperature as the score T for the temperature of the beverage D10. Specifically, the reference temperature is appropriately set to a value capable of determining whether or not the temperature of the beverage D10 is high enough to be expected to make the beverage D10 relatively difficult to drink. The information indicating the reference temperature may be stored in advance in the storage element of the control device 100, or may be stored in the external device of the vehicle. The second estimation unit 131b calculates "2" as the score T when the information indicating the temperature of the beverage D10 is not acquired by the control device 100.

Further, the second estimation unit 131b calculates, for example, "1" when the straw is inserted in the beverage container C10 and "2" when the straw is not inserted, as the score P regarding the presence or absence of the straw.

Whether or not a straw is inserted in the beverage container C10 is detected, for example, by performing image processing for extracting an area in which the straw is reflected for at least one of the images obtained by the upper camera 41 or the side camera 42. obtain.

FIG. 8 is an explanatory diagram showing an example of an image Im15 in which a paper cup E11 imaged by the upper camera 41 and into which a straw E31 is inserted is displayed. For example, as shown in FIG. 8, when the straw E31 is shown in the image Im15 in which the paper cup E11 obtained by the upper camera 41 is shown, it can be detected that the straw is inserted in the beverage container C10. In that case, the second estimation unit 131b calculates "1" as the score P regarding the presence or absence of the straw.

Further, the second estimation unit 131b calculates, for example, "1" when the traveler lid is attached to the beverage container C10 and "2" when the traveler lid is not attached, as the score Q regarding the presence or absence of the traveler lid. do.

Whether or not the traveler lid is attached to the beverage container C10 is determined by, for example, performing image processing for extracting an area in which the traveler lid is reflected on at least one of the images obtained by the upper camera 41 or the side camera 42. Can be detected.

FIG. 9 is an explanatory diagram showing an example of an image Im16 in which a paper cup E11 imaged by the upper camera 41 and equipped with the traveler lid E32 is shown. For example, as shown in FIG. 9, when the traveler lid E32 is shown in the image Im16 in which the paper cup E11 obtained by the upper camera 41 is shown, it can be detected that the traveler lid is attached to the beverage container C10. In that case, the second estimation unit 131b calculates "1" as the score Q regarding the presence or absence of the traveler lid.

Further, the second estimation unit 131b calculates, for example, the total value of the score T, the score P, and the score Q as the drinkability score N indicating the drinkability of the beverage D10.

For example, when the drinkability score N is larger than the reference value N_h, the second estimation unit 131b estimates that the drinkability of the beverage D10 is “difficult to drink”. Further, the second estimation unit 131b estimates that the drinkability of the beverage D10 is "normal" when, for example, the drinkability score N is equal to or less than the reference value N_h and is equal to or higher than the reference value N_l. Further, the second estimation unit 131b estimates that the drinkability of the beverage D10 is "easy to drink" when the drinkability score N is smaller than the reference value N_l. Specifically, the reference values N_h and N_l for estimating the ease of drinking are appropriately set to values that can appropriately estimate the ease of drinking of the beverage D10 according to the drinkability score N calculated as described above. Will be done. The information indicating the reference values N_h and N_l may be stored in advance in the storage element of the control device 100, or may be stored in the external device of the vehicle.

Next, in step S507, the index value specifying unit 131c specifies an index value as a scattering index indicating the inherent ease of scattering of the beverage D10 when eating or drinking.

For example, the index value specifying unit 131c specifies the index value according to at least one of the estimation results by the first estimation unit 131a or the second estimation unit 131b.

Specifically, the index value specifying unit 131c specifies the index value according to the estimation result of the spillability of the beverage D10 by the first estimation unit 131a and the estimation result of the drinkability of the beverage D10 by the second estimation unit 131b. do.

The index value specifying unit 131c specifies the index value based on, for example, the estimation result of the spillability of the beverage D10 and the preset relationship between the estimation result of the ease of drinking of the beverage D10 and the index value. do. The relationship between the spillability estimation result and the drinkability estimation result and the index value is specifically different from that of the beverage D10 according to the spillability estimation result and the drinkability estimation result. It is appropriately set so that an index value as a scattering index indicating ease can be appropriately specified. The spillability estimation result and the information indicating the relationship between the spillability estimation result and the index value may be stored in advance in the storage element of the control device 100, and are stored in the external device of the vehicle. You may.

FIG. 10 is an explanatory diagram showing an example of the relationship between the estimation result of the spillability of the beverage D10 and the estimation result of the ease of drinking of the beverage D10 and the index value. The index value specifying unit 131c specifies the index value based on, for example, the estimation result of spillability and the estimation result of ease of drinking shown in FIG. 10 and the relationship between the index value. In the example shown in FIG. 10, the larger the index value, the easier it is for the beverage D10 to scatter.

Specifically, in the index value specifying unit 131c, when the easiness of spilling of the beverage D10 is estimated to be "difficult to spill" and the ease of drinking of the beverage D10 is estimated to be "easy to drink", the index value is "easy to drink". 1 ”is specified. Further, when the easiness of spilling of the beverage D10 is estimated to be "easy to spill" and the easiness of drinking of the beverage D10 is estimated to be "difficult to drink", the index value specifying unit 131c sets "5" as the index value. Identify. In this way, the index value specifying unit 131c specifies a larger value as the index value as the beverage D10 is more likely to spill. Further, the index value specifying unit 131c specifies a larger value as the index value as the beverage D10 is harder to drink.

As described above, the specifying unit 131 specifies a scattering index indicating the inherent ease of scattering of the beverage D10 at the time of eating and drinking based on the food and drink information related to the beverage D10.

Next, in step S509, the setting unit 132 sets a threshold value for vehicle behavior prediction information according to the specified index value. Specifically, the setting unit 132 sets a threshold value for the acceleration prediction value G, which is a prediction value of the acceleration generated in the vehicle when traveling on each route candidate, as such a threshold value. For example, as the acceleration prediction value G, a predicted value of the combined acceleration corresponding to the acceleration obtained by synthesizing the components in the front-back direction, the left-right direction, and the up-down direction of the acceleration is used. As the acceleration prediction value G, a prediction value of lateral acceleration corresponding to a component in the left-right direction of acceleration may be used.

As will be described later, in the process performed by the control device 100, specifically, the acceleration prediction value G for each route candidate is predicted as vehicle behavior prediction information, and the acceleration prediction value G for each route candidate is compared with the threshold value. The recommended route can be determined based on the result. As described above, the threshold value set in step S509 can be used in the process of determining the recommended route, which will be described later.

The setting unit 132 sets, as a threshold value, a value that can appropriately evaluate the possibility of scattering of the beverage D10 at the time of eating and drinking at each point of each route candidate. Specifically, the setting unit 132 sets a smaller value as a threshold value as the index value becomes larger. For example, when the predicted acceleration value G at a certain point is equal to or less than the threshold value, it can be evaluated that the possibility of scattering of the beverage D10 is relatively low when traveling at the point. Therefore, it is expected that it is possible to eat and drink without scattering the food and drink when traveling at the relevant point. On the other hand, when the predicted acceleration value G at a certain point exceeds the threshold value, it can be evaluated that the possibility of scattering of the beverage D10 is relatively high when traveling at the point. Therefore, it is expected that it is desirable to be careful about eating and drinking when traveling at the relevant point, or it is desirable to prohibit eating and drinking. When the acceleration prediction value G at a certain point exceeds the threshold value, it can be evaluated that the larger the magnitude of the acceleration prediction value G with respect to the threshold value, the greater the degree of possibility that the beverage D10 is scattered.

The setting unit 132 may set the threshold value based on another parameter different from the index value. For example, the setting unit 132 may set the threshold value based on at least one of the clothes of the passengers in the vehicle, the number of passengers in the vehicle, the time zone to which the current time belongs, or the destination of the vehicle. Information indicating the clothes and the number of passengers in the vehicle can be obtained, for example, by performing image processing on an image obtained by a camera that captures the inside of the vehicle. Further, the information indicating the destination of the vehicle can be acquired based on, for example, the input information to the navigation device 20.

Specifically, when the color of the passenger's clothes in the vehicle is a color in which stains such as white are easily noticeable, the value of the setting unit 132 is smaller than that in the case where stains such as black are inconspicuous. May be set as a threshold value. Further, the setting unit 132 may set a large value as a threshold value when there is only one driver in the vehicle as compared with the case where there are a plurality of passengers. Further, when the current time belongs to a time zone where there is a high possibility of returning home such as the evening of a weekday, the setting unit 132 may set a larger value as a threshold value as compared with the case where it belongs to another time zone. Further, when the destination of the vehicle is home, the setting unit 132 may set a larger value as a threshold value as compared with the case where the destination is another destination.

In this way, the setting unit 132 sets the threshold value based on other parameters different from the index value so that the threshold value becomes a smaller value when it is more unfavorable for the food or drink to scatter when eating or drinking. May be good.

Next, in step S511, the acquisition unit 110 acquires the input information by the driver including the priority condition information indicating the priority condition prioritized as the condition of the guidance target route and the destination information indicating the destination.

Next, in step S513, the route determination unit 133 determines whether or not the input information from the driver has been acquired. If it is determined that the input information has been acquired (step S513 / YES), the process proceeds to step S515. On the other hand, if it is not determined that the input information has been acquired (step S513 / NO), the process proceeds to step S529.

In step S515, the route determination unit 133 searches for a route candidate based on the input information from the driver.

For example, the route determination unit 133 gives priority to routes having a high degree of conformity with the priority conditions from among the routes connecting the current position and the destination, and selects a predetermined number (for example, 3 to 5) of routes as route candidates. Search as. Examples of the priority conditions that can be input by the driver include conditions such as required time priority, distance priority, fuel consumption priority, and general road priority. Specifically, when the driver inputs the condition that the required time is prioritized as the priority condition, the route determination unit 133 determines the required time from the current position to the destination from the route connecting the current position and the destination. Searches for a predetermined number of routes as route candidates in ascending order. The predetermined number of set values may be changed, for example, by operating the navigation device 20.

Next, in step S517, the route determination unit 133 determines whether or not the reference time has elapsed from the time when the threshold value was previously set. When it is determined that the reference time has elapsed from the time when the threshold value was set last time (step S517 / YES), the process proceeds to step S521. On the other hand, if it is not determined that the reference time has elapsed since the time when the threshold value was set last time (step S517 / NO), the process proceeds to step S519.

Specifically, the reference time is determined to be NO in step S501, and the threshold value is not updated. Therefore, the inherent easiness of scattering of the beverage D10 at the time of eating and drinking changes. The time is set as appropriate so that it can be determined whether or not the possibility of the drink is relatively high. The information indicating the reference time may be stored in advance in the storage element of the control device 100, or may be stored in the external device of the vehicle.

In step S519, the prediction unit 120 predicts the acceleration prediction value G for each route candidate. Specifically, the prediction unit 120 calculates the acceleration prediction value G for each point of each route candidate. In other words, the prediction unit 120 calculates the predicted value of the acceleration generated in the vehicle when the vehicle travels at each point in each route candidate as the acceleration predicted value G when the route candidates are traveled. In the following, an example in which the predicted value of the combined acceleration is predicted as the acceleration predicted value G will be mainly described, but the predicted value of the lateral acceleration may be predicted as the acceleration predicted value G.

For example, the prediction unit 120 sets a plurality of points to be predicted by the acceleration prediction value G at predetermined intervals (for example, 30 m) for each route candidate, and predicts the acceleration prediction value G for each set point. do. For example, the prediction unit 120 can set a point to be predicted by the acceleration prediction value G for each route candidate by using map information that can be stored in the storage element of the control device 100. The predetermined interval may be appropriately set according to, for example, the calculation speed of the control device 100.

FIG. 11 is an explanatory diagram showing an example of the prediction result of the acceleration prediction value G for each route candidate. FIG. 11 shows an example in which the predicted value of the combined acceleration is predicted as the predicted acceleration value G. The unit of the acceleration prediction value G in FIG. 11 is [m / s ² ]. Further, in FIG. 11, the acceleration prediction value G for each route candidate when the first route R1, the second route R2, and the third route R3 are searched as route candidates between the current position P1 and the destination P2. The prediction result of is shown.

For example, as shown in FIG. 11, the prediction unit 120 sets the points P101, P102, P103, P104, P105, P106, P107, P108, as the points to be predicted by the acceleration prediction value G for the first route R1. Set P109.

Further, for example, the prediction unit 120 sets points P101, P102, P203, P204, P205, P206, P107, P108, and P109 as points to be predicted by the acceleration prediction value G for the second route R2. Note that some routes including points P101, P102, P107, P108, and P109 are common between the first route R1 and the second route R2.

Further, for example, the prediction unit 120 sets points P301, P302, P303, P304, P305, P306, P307, P308, and P309 as points to be predicted by the acceleration prediction value G for the third route R3.

Then, the prediction unit 120 predicts the acceleration prediction value G for each of the points set as the prediction target of the acceleration prediction value G for each of the first route R1, the second route R2, and the third route R3.

For example, in the example shown in FIG. 11, for the first route R1, the acceleration predicted values G at the points P101, P102, P103, P104, P105, P106, P107, P108, and P109 are 13 [m / s ² ], 5 [M / s ² ], 4 [m / s ² ], 12 [m / s ² ], 25 [m / s ² ], 4 [m / s ² ], 3 [m / s ² ], 4 [m / S ² ] and 4 [m / s ² ] are predicted, respectively.

Further, for the second route R2, the acceleration predicted values G of the points P101, P102, P203, P204, P205, P206, P107, P108, and P109 are 13 [m / s ² ], 5 [m / s ² ], and 11 [M / s ² ], 8 [m / s ² ], 12 [m / s ² ], 13 [m / s ² ], 3 [m / s ² ], 4 [m / s ² ], 4 [m / S ² ] are predicted respectively.

Further, for the third route R3, the acceleration predicted values G of the points P301, P302, P303, P304, P305, P306, P307, P308, and P309 are 5 [m / s ² ], 22 [m / s ² ], and 3. [M / s ² ], 25 [m / s ² ], 7 [m / s ² ], 13 [m / s ² ], 18 [m / s ² ], 2 [m / s ² ], 5 [m / S ² ] are predicted respectively.

Specifically, the prediction unit 120 can predict the acceleration prediction value G for each point by referring to the database for each point in which the candidate for the acceleration prediction value G is associated with each vehicle speed.

FIG. 12 is an explanatory diagram showing an example of a database in which candidates for the acceleration prediction value G are associated with each vehicle speed. ^{Specifically, in the database shown in FIG. 12, 13 [m / s 2} ] is associated with a vehicle speed of 50 to 60 [km / h] as a candidate for the acceleration prediction value G. ^{Further, 18 [m / s 2} ] is associated with the vehicle speed of 60 to 75 [km / h] as a candidate for the acceleration prediction value G. ^{Further, 23 [m / s 2} ] is associated with the vehicle speed of 75 to 85 [km / h] as a candidate for the acceleration prediction value G.

For example, the prediction unit 120 predicts a candidate for an acceleration prediction value G associated with the current vehicle speed in the database as an acceleration prediction value G at a point corresponding to the database. Specifically, such a database is prepared for each point.

Various databases can be used as a database for each point in which the candidate of the acceleration prediction value G is associated with each vehicle speed.

For example, the control device 100 constructs and stores such a database based on the traveling record of the own vehicle, which is the detection result of the vehicle speed sensor 45 and the acceleration sensor 46 when the own vehicle actually travels at each point. It may be used in the prediction process of the acceleration prediction value G. In this case, the database can be stored in the storage element of the control device 100.

Further, for example, the control device 100 may be used in the prediction processing of the acceleration prediction value G by referring to the database constructed based on the traveling record of the other vehicle. In this case, the database is stored in the external device of the vehicle, and the control device 100 can refer to the database by communicating with the external device via the communication device 30.

When a database constructed based on the driving record of another vehicle can be referred to, the prediction unit 120 gives priority to a database constructed based on the driving record of another vehicle whose vehicle type matches or is close to that of the own vehicle, for example. The acceleration prediction value G may be predicted with reference to the reference. Specifically, the correspondence relationship between the vehicle type and the reference destination database (hereinafter, also referred to as the reference destination database) is defined in advance, and the prediction unit 120 refers to the reference destination database according to the correspondence relationship. FIG. 13 is an explanatory diagram showing an example of the correspondence between the vehicle type and the reference database. For example, in the example shown in FIG. 13, the reference databases DB11, DB12, and DB13 correspond to the vehicle types CM1, CM2, and CM3, respectively. Specifically, when the vehicle type of the own vehicle is CM1, the prediction unit 120 predicts the acceleration prediction value G with reference to the reference database DB 11.

Further, when the database constructed based on the traveling record of another vehicle can be referred to, the prediction unit 120 is, for example, a database constructed based on the traveling record of another vehicle whose vibration characteristics and the like match or are close to those of the own vehicle. May be preferentially referred to to predict the acceleration prediction value G. Specifically, the correspondence between the vibration characteristics and the reference database is defined in advance, and the prediction unit 120 refers to the reference database according to the correspondence. FIG. 14 is an explanatory diagram showing an example of the correspondence between the vibration characteristics and the referenced database. For example, in the example shown in FIG. 14, the reference databases DB21, DB22, and DB23 correspond to the vibration characteristics VC1, VC2, and VC3, respectively. Specifically, when the vibration characteristic of the own vehicle is VC1, the prediction unit 120 predicts the acceleration prediction value G with reference to the reference database DB 21.

The vibration characteristics of the vehicle are expressed as, for example, the characteristics of the combined acceleration with respect to the combination of the international roughness index IRI (International Roughness Index), which is an index indicating the degree of unevenness of the traveling path, and the vehicle speed. FIG. 15 shows an example of the characteristic of the combined acceleration with respect to the combination of the international roughness index IRI and the vehicle speed for each of the vibration characteristics VC1, VC2, and VC3. Specifically, in the example shown in FIG. 15, the vibration characteristic VC1 is a case where the vehicle travels on a road with an international roughness index IRI of 0 to 5 [mm / m] at a vehicle speed of 0 to 20 [km / h]. It is a characteristic that a combined acceleration of 0 to 10 [m / s ^{2] is generated.}

Further, the prediction unit 120 may predict the acceleration prediction value G based on the information about the traveling path of the point to be predicted by the acceleration prediction value G. Specifically, the prediction unit 120 predicts the acceleration prediction value G at a point where a database in which candidates for the acceleration prediction value G are linked to each vehicle speed is not prepared, based on information on the travel path of the point. You may. For example, the prediction unit 120 may predict the acceleration prediction value G according to the curvature of the traveling path at such a point and the current vehicle speed. Further, for example, the prediction unit 120 may predict the acceleration prediction value G according to the slope of the traveling path at such a point and the current vehicle speed. Further, for example, the prediction unit 120 may predict the acceleration prediction value G based on the presence / absence of a traffic light on the traveling road at such a point, the number of railroad crossings installed, the occurrence of traffic congestion, the road surface condition, and the like. These information about the traveling path may be included in the map information stored in the storage element of the control device 100, for example.

Next, in step S600, the route determination unit 133 determines the recommended route based on the comparison result between the acceleration prediction value G and the threshold value.

Hereinafter, the first example and the second example will be described as examples of the process of determining the recommended route based on the comparison result between the acceleration prediction value G and the threshold value.

FIG. 16 is a flowchart showing an example of the flow of the first example of the recommended route determination process based on the comparison result between the acceleration prediction value G and the threshold value performed by the control device 100 according to the present embodiment. The processing flow shown in FIG. 16 is executed in step S600 of the processing flow shown in FIG.

When the processing flow shown in FIG. 16 is started, first, in step S611, the route determination unit 133 calculates the number of excess points, which is the number of points where the acceleration prediction value G exceeds the threshold value for each route candidate.

Hereinafter, as an example, the calculation of the number of excess points when the prediction result of the acceleration prediction value G is the prediction result shown in FIG. 11 and the threshold value ^{is set to 10 [m / s 2] will be described.}

In this case, in the example shown in FIG. 11, the acceleration prediction values G at the points P101, P104, and P105 exceed the threshold value for the first route R1. Therefore, the route determination unit 133 calculates "3" as the number of excess points for the first route R1.

Further, for the second route R2, the acceleration prediction value G at the points P101, P203, P205, and P206 exceeds the threshold value. Therefore, the route determination unit 133 calculates "4" as the number of excess points for the second route R2.

Further, for the third route R3, the acceleration prediction value G at the points P302, P304, P306, and P307 exceeds the threshold value. Therefore, the route determination unit 133 calculates "4" as the number of excess points for the third route R3.

Next, in step S613, the route determination unit 133 determines the route candidate having the smallest number of excess points as a recommended route.

For example, when "3", "4" and "4" are calculated as the number of excess points for each of the first route R1, the second route R2 and the third route R3 as described above, the route determination unit 133 determines the first route R1 as a recommended route.

In this way, the route determination unit 133 may determine the recommended route based on the number of points where the acceleration prediction value G for each of the plurality of route candidates exceeds the threshold value. Here, the point where the acceleration prediction value G exceeds the threshold value corresponds to the point where the possibility of scattering of the beverage D10 is evaluated to be relatively high. Therefore, the larger the number of points where the predicted acceleration value G exceeds the threshold value on the traveling route, the easier it is for the beverage D10 to scatter in the vehicle. Therefore, the smaller the number of points where the acceleration prediction value G exceeds the threshold value on the route, the easier it is to suppress the scattering of the beverage D10 in the vehicle.

In the first example of the recommended route determination process, the route determination unit 133 may determine the recommended route based on the priority conditions input by the driver in addition to the number of excess points. For example, when the driver inputs the condition that the required time is prioritized as the priority condition, the route determination unit 133 determines that the number of excess points is two when the required time for the route candidate having the smallest number of excess points is excessively long. The smallest number of route candidates may be determined as the recommended route.

Next, the processing flow shown in FIG. 16 ends.

FIG. 17 is a flowchart showing an example of the flow of the second example of the process of determining the recommended route based on the comparison result between the acceleration prediction value G and the threshold value performed by the control device 100 according to the present embodiment. The processing flow shown in FIG. 17 is executed in step S600 of the processing flow shown in FIG.

When the processing flow shown in FIG. 17 is started, first, in step S621, the route determination unit 133 calculates an evaluation value SCORE corresponding to the magnitude of the acceleration prediction value G with respect to the threshold value at each point for each route candidate. ..

For example, the route determination unit 133 calculates the evaluation value SCORE for each route candidate using the following equation (1).

In the formula (1), n_low is the number of points where the predicted acceleration value G exceeds the threshold value and is equal to or less than the first reference acceleration. Further, n_middle is the number of points where the predicted acceleration value G exceeds the first reference acceleration and is equal to or less than the second reference acceleration. Further, n_high is the number of points where the predicted acceleration value G exceeds the second reference acceleration. Further, the first reference acceleration and the second reference acceleration are specifically set to values that can appropriately determine the degree of the magnitude of the acceleration prediction value G with respect to the threshold value. The first reference acceleration is set to a value larger than the threshold value, for example, a value 5 [m / s ² ] larger than the threshold value. The second reference acceleration is set to a value larger than the second reference acceleration, and is set to a value 5 [m / s ² ] larger than the second reference acceleration, for example. Further, α1, α2, and α3 are coefficients that are appropriately set according to the degree of the magnitude of the acceleration prediction value G with respect to the threshold value at the corresponding point. α1, α2 and α3 are set to increase in this order, and are set to, for example, 10, 20 and 30, respectively.

Here, the route determination unit 133 may calculate the evaluation value SCORE based on the priority condition input by the driver in addition to the magnitude of the acceleration prediction value G with respect to the threshold value at each point. For example, when the driver inputs the condition that the required time is prioritized as the priority condition, the route determination unit 133 calculates the evaluation value SCORE for each route candidate using the following equation (2).

In formula (2), TIME is the time required from the current position to the destination. The unit of TIME is, for example, [minute]. Further, α4 is a coefficient that is appropriately set, and is set to, for example, 0.8.

Hereinafter, as an example, the calculation of the evaluation value SCORE using the equation (2) when the prediction result of the acceleration prediction value G is the prediction result shown in FIG. 11 and 10 [m / s ^{2] is set as the threshold value.} Will be described. The first reference acceleration, the second reference acceleration, α1, α2, α3 and α4 are set to 15 [m / s ² ], 20 [m / s ² ], 10, 20, 30 and 0.8, respectively. Shall be.

In this case, in the example shown in FIG. 11, for the first route R1, the predicted acceleration values G at the points P101 and P104 exceed the threshold value and are equal to or less than the first reference acceleration. Further, the predicted acceleration value G at the point P105 exceeds the second reference acceleration. Therefore, n_low, n_middle, and n_high become "2", "0", and "1", respectively. Here, when the required time of the first route R1 is 20 [minutes], the TIME is "20". Therefore, the route determination unit 133 calculates "66" as the evaluation value SCORE for the first route R1 by substituting these values into n_low, n_middle, n_high and TIME in the equation (2).

Further, for the second route R2, the acceleration prediction value G at the points P101, P203, P205, and P206 exceeds the threshold value and is equal to or less than the first reference acceleration. Therefore, n_low, n_middle, and n_high become "4", "0", and "0", respectively. Here, when the required time of the second route R2 is 24 [minutes], the TIME is "24". Therefore, the route determination unit 133 calculates "59.2" as the evaluation value SCORE for the second route R2 by substituting these values into n_low, n_middle, n_high and TIME in the equation (2).

Further, for the third route R3, the acceleration prediction value G at the point P306 exceeds the threshold value and is equal to or less than the first reference acceleration. Further, the predicted acceleration value G at the point P307 exceeds the first reference acceleration and is equal to or less than the second reference acceleration. Further, the predicted acceleration values G at points P302 and P304 exceed the second reference acceleration. Therefore, n_low, n_middle and n_high become "1", "1" and "2", respectively. Here, when the required time of the third route R3 is 22 [minutes], the TIME is "22". Therefore, the route determination unit 133 calculates "107.6" as the evaluation value SCORE for the third route R3 by substituting these values into n_low, n_middle, n_high and TIME in the equation (2).

Next, in step S623, the route determination unit 133 determines the route candidate having the lowest evaluation value SCORE as the recommended route.

For example, as described above, when "66", "59.2" and "107.6" are calculated as the evaluation values SCORE for each of the first route R1, the second route R2 and the third route R3, respectively. , The route determination unit 133 determines the second route R2 as a recommended route.

In this way, the route determination unit 133 may determine the recommended route based on the magnitude of the acceleration prediction value G with respect to the threshold value at each point for each of the plurality of route candidates. Here, the magnitude of the acceleration prediction value G with respect to the threshold value at each point corresponds to the degree of the possibility of scattering of the beverage D10 at each point. Therefore, the larger the total of the values corresponding to the magnitude of the acceleration prediction value G with respect to the threshold value at each point of the traveling route (for example, the coefficients α1, α2, α3 in the equations (1) and (2)), the more in the vehicle. Beverage D10 is likely to scatter. Therefore, the smaller the sum of the values corresponding to the magnitude of the acceleration prediction value G with respect to the threshold value at each point of the traveling route, the easier it is to suppress the scattering of the beverage D10 in the vehicle.

In the above description, an example in which the magnitude of the predicted acceleration value G with respect to the threshold value is evaluated in three stages using the first reference acceleration and the second reference acceleration to calculate the evaluation value SCORE has been described, but the calculation of the evaluation value SCORE has been described. The method is not particularly limited to such an example. For example, the route determination unit 133 may evaluate the magnitude of the acceleration prediction value G with respect to the threshold value in two stages, or evaluate it in four or more stages to calculate the evaluation value SCORE. In that case, the number of reference accelerations for evaluating the magnitude of the acceleration prediction value G with respect to the threshold value may change as appropriate. Further, the route determination unit 133 may calculate the evaluation value SCORE without using the above-mentioned reference acceleration. For example, the route determination unit 133 calculates a value obtained by summing the acceleration prediction values G for points where the acceleration prediction value G exceeds the threshold value for the entire route candidate or a value corresponding to such a value as the evaluation value SCORE. You may.

Next, the processing flow shown in FIG. 17 ends.

If YES is determined in the determination process of step S517, in step S521, the route determination unit 133 determines the recommended route according to the priority condition input by the driver.

For example, the route determination unit 133 preferentially determines a route having a high degree of conformity with the priority condition from a plurality of route candidates as a recommended route. Specifically, when the driver inputs the condition that the required time is prioritized as the priority condition, the route determination unit 133 determines the route candidate having the shortest required time from the current position to the destination among the plurality of route candidates. Is determined as the recommended route.

After step S600 or step S521, in step S523, the control unit 140 causes the navigation device 20 to display a route candidate including the recommended route.

FIG. 18 is an explanatory diagram showing a display example of a route candidate when the first route R1 is determined as a recommended route.

For example, in step S600, when the first example of the recommended route determination process described with reference to FIG. 16 is executed, the first route R1 is determined as the recommended route as described above. In that case, for example, as shown in FIG. 18, the control unit 140 highlights the first route R1 determined as the recommended route on the navigation device 20 by comparing it with other route candidates and making the color different. Let me.

Further, the control unit 140 may display an object indicating a point where the acceleration prediction value G exceeds the threshold value on the display device in the first route R1, the second route R2, and the third route R3. For example, as shown in FIG. 18, the control unit 140 is located in the vicinity of points P101, P104, P105, P203, P205, P206, P302, P304, P306, and P307, which are points where the acceleration prediction value G exceeds the threshold value. As such an object, the object OJ1 representing the mark of the beverage D10 may be displayed on the navigation device 20.

Here, the control unit 140 may display the object OJ1 on the navigation device 20 by changing the display mode of the object OJ1 according to the magnitude of the acceleration prediction value G. For example, as shown in FIG. 18, the control unit 140 may display the object OJ1 on the navigation device 20 by making the dimensions of the object OJ1 different according to the magnitude of the acceleration prediction value G. Specifically, in the example shown in FIG. 18, the dimension of the object OJ1 indicating the point P307 where the acceleration prediction value G exceeds the first reference acceleration and is equal to or less than the second reference acceleration is such that the acceleration prediction value G exceeds the threshold value. It is larger than the size of the object OJ1 indicating the points P101, P104, P203, P205, P206, and P306 which are equal to or less than one reference acceleration. The dimension of the object OJ1 indicating the points P105, P302, and P304 in which the acceleration predicted value G exceeds the second reference acceleration is the object indicating the point P307 in which the acceleration predicted value G exceeds the first reference acceleration and is equal to or less than the second reference acceleration. It is even larger than the dimensions of OJ1.

The control unit 140 may display the object OJ1 on the navigation device 20 by making the color of the object OJ1 different according to the magnitude of the acceleration prediction value G, and the shape of the object OJ1 may be the magnitude of the acceleration prediction value G. The object OJ1 may be displayed on the navigation device 20 in a different manner depending on the situation.

FIG. 19 is an explanatory diagram showing a display example of a route candidate different from the example shown in FIG. 18 when the first route R1 is determined as a recommended route.

In the example shown in FIG. 19, similarly to FIG. 18, the first route R1 determined as the recommended route is highlighted by the navigation device 20 by making the color different from that of other route candidates. On the other hand, in the example shown in FIG. 19, an object indicating a point where the acceleration prediction value G exceeds the threshold value in the first route R1, the second route R2, and the third route R3 in the vicinity of the point where the acceleration prediction value G exceeds the threshold value. As shown in FIG. 18, the linear object OJ2 along the route is displayed by the navigation device 20. As described above, the type of the object indicating the point where the acceleration prediction value G exceeds the threshold value is not particularly limited to the examples illustrated in the drawings. The object OJ2 may be displayed superimposed on the route, or may be displayed side by side with respect to the route.

Further, in the example shown in FIG. 19, the object OJ2 is displayed by varying the width of the object OJ2 as a dimension according to the magnitude of the acceleration prediction value G. Specifically, in the example shown in FIG. 19, the width of the object OJ2 indicating the point P307 where the acceleration prediction value G exceeds the first reference acceleration and is equal to or less than the second reference acceleration is such that the acceleration prediction value G exceeds the threshold value. It is thicker than the width of the object OJ2 indicating the points P101, P104, P203, P205, P206, and P306 which are equal to or less than one reference acceleration. The width of the object OJ2 indicating the points P105, P302, and P304 in which the acceleration predicted value G exceeds the second reference acceleration is the object indicating the point P307 in which the acceleration predicted value G exceeds the first reference acceleration and is equal to or less than the second reference acceleration. It is even thicker than the width of OJ2.

Similar to the object OJ1, the control unit 140 may display the object OJ2 on the navigation device 20 by changing the color of the object OJ2 according to the magnitude of the acceleration prediction value G, and the shape of the object OJ2 (for example, , Line type) may be different depending on the magnitude of the acceleration prediction value G, and the object OJ2 may be displayed on the navigation device 20.

FIG. 20 is an explanatory diagram showing a display example of a route candidate when the second route R2 is determined as a recommended route.

For example, in step S600, when the second example of the recommended route determination process described with reference to FIG. 17 is executed, the second route R2 is determined as the recommended route as described above. In that case, for example, as shown in FIG. 20, the control unit 140 highlights the second route R2 determined as the recommended route on the navigation device 20 by comparing it with other route candidates and making the color different. Let me.

In this way, the control unit 140 causes the display device to display the route candidates including the recommended routes. Further, the control unit 140 may display an object indicating a point where the vehicle behavior prediction information exceeds the threshold value in a plurality of route candidates including the recommended route on the display device. For example, the control unit 140 may display the object on the display device by changing the display mode of the object according to the size of the vehicle behavior prediction information. Specifically, the control unit 140 may display the object on the display device by making at least one of the dimensions, colors, or shapes of the object different according to the size of the vehicle behavior prediction information.

Next, in step S525, the control unit 140 determines the guidance target route.

For example, the control unit 140 determines the guidance target route based on the information input by the driver. Specifically, when the driver performs an input operation for selecting the first route R1 as the guide target route, the control unit 140 determines the first route R1 as the guide target route.

Next, in step S527, the control unit 140 causes the navigation device 20 to start route guidance.

After step S527, the processing flow shown in FIG. 3 ends.

If NO is determined in the determination process of step S513, in step S529, the control device 100 determines whether or not the route guidance by the navigation device 20 is being executed and the threshold value has been updated. If it is determined that the route guidance is being executed and the threshold value has been updated (step S529 / YES), the process proceeds to step S519. On the other hand, when the route guidance is being executed and it is not determined that the threshold value has been updated (step S529 / NO), the processing flow shown in FIG. 3 ends.

As described above, for example, while the beverage container C10 is placed in the cup holder H10, the processes of steps S503 to S509 are performed, so that the processes after step S511 are performed after the threshold value is updated. .. Therefore, when the determination process in step S529 determines YES, it corresponds to the case where the threshold value is updated after the start of route guidance. In such a case, since the processing after step S519 is performed, the recommended route is determined again based on the updated threshold value. Thereby, it is possible to optimize the recommended route as a route candidate capable of suppressing the scattering of food and drink in the vehicle.

On the other hand, for example, while the beverage container C10 is being taken out from the cup holder H10, the processes after step S511 are performed while the threshold value is maintained. Therefore, when the determination process in step S517 determines YES, it corresponds to the case where the threshold value is maintained without updating the threshold value and the reference time elapses. In such a case, since the process of step S521 is performed, the recommended route is determined according to the priority condition. When the reference time elapses while the threshold value is maintained without updating the threshold value, a route candidate that tends to suppress the scattering of food and drink in the vehicle due to the decrease in the reliability of the threshold value is appropriate as the recommended route. Can be difficult to determine. In such a case, by determining the recommended route according to the priority condition, it is possible to suppress the deterioration of the comfort in the vehicle.

In the above description, an example in which the guidance target route is determined based on the input information by the driver has been described, but the guidance target route may be determined by the control unit 140 without depending on the input information by the driver. For example, the control unit 140 may determine the recommended route determined by the route determination unit 133 as the guidance target route without relying on the input information by the driver.

Further, in the above, the case where there are a plurality of foods and drinks in the vehicle is not particularly mentioned, but in such a case, the determination unit 130 preferentially uses the threshold value corresponding to the specific food and drinks and uses the recommended route. You may decide.

For example, when there are a plurality of foods and drinks in the vehicle, the control unit 140 may determine the recommended route by preferentially using the smaller threshold value among the threshold values corresponding to each food and drink. Specifically, the control unit 140 may determine the recommended route using the smallest threshold value among the threshold values corresponding to each food and drink.

Further, for example, when there are a plurality of foods and drinks in the vehicle, the control unit 140 may determine the recommended route by preferentially using the threshold value corresponding to the foods and drinks eaten by the driver. Specifically, the control unit 140 even when the threshold value corresponding to the food and drink eaten and eaten by the passenger other than the driver is smaller than the threshold value corresponding to the food and drink eaten and eaten by the driver. The recommended route may be determined using the threshold value corresponding to the food and drink eaten by the driver.

The determination unit 130 may adjust the threshold value based on the relationship between the comparison result between the vehicle behavior prediction information and the threshold value and the actual behavior of food and drink. The control device 100 can detect, for example, the magnitude of vibration generated on the surface of the liquid in the food or drink as information on the actual behavior of the food or drink by image processing on the image obtained by the upper camera 41. For example, when the determination unit 130 actually travels at a point where the acceleration prediction value G is equal to or less than the threshold value and the magnitude of the vibration generated on the surface of the liquid in the food or drink is excessively large, the determination unit 130 eats or drinks such as the presence or absence of a straw. The threshold may be reduced even when the state of the object is maintained.

<3. Effect of control device>
Subsequently, the effect of the control device 100 according to the present embodiment will be described.

In the control device 100 according to the present embodiment, from the plurality of route candidates to the destination based on the food and drink information regarding the food and drink in the vehicle and the vehicle behavior prediction information regarding the vehicle behavior for each of the plurality of route candidates. Recommended route is determined. As a result, the possibility of scattering of food and drink for each route candidate can be appropriately evaluated according to the unique properties and conditions of food and drink. Therefore, from a plurality of route candidates, a route candidate capable of suppressing the scattering of food and drink in the vehicle can be determined as a recommended route. Therefore, it is possible to present a recommended route that can suppress the scattering of food and drink in the vehicle.

Further, in the control device 100 according to the present embodiment, a scattering index indicating the inherent easiness of scattering of the food or drink at the time of eating or drinking can be specified based on the food or drink information. In addition, a threshold value for vehicle behavior prediction information can be set according to the scattering index. Further, the recommended route can be determined based on the comparison result between the vehicle behavior prediction information and the threshold value for each of the plurality of route candidates. In this way, by determining the recommended route based on the relationship between the scattering index and the vehicle behavior prediction information, it is possible to appropriately evaluate the scattering possibility of food and drink at each point of each route candidate. Therefore, since it is effectively realized to appropriately evaluate the possibility of food and drink scattering for each route candidate, the route candidate capable of suppressing the scattering of food and drink in the vehicle from among a plurality of route candidates. Is effectively realized as the recommended route.

Further, in the control device 100 according to the present embodiment, the spillability of food and drink is estimated based on the food and drink information, and the scattering index can be specified according to the estimation result of the spillability of food and drink. Thereby, the scattering index can be appropriately specified according to the spillability of food and drink. Therefore, the possibility of scattering of food and drink can be more appropriately evaluated at each point of each route candidate according to the susceptibility of food and drink to spill.

Further, in the control device 100 according to the present embodiment, the easiness of spilling of food and drink can be estimated based on the container information regarding the container of food and drink. Thereby, it is possible to appropriately estimate the spillability of food and drink according to the container information. Therefore, the scattering index can be specified more appropriately.

Further, in the control device 100 according to the present embodiment, the easiness of spilling of food and drink can be estimated based on at least one of each parameter of the type of food and drink container or the size of the mouthpiece of the food and drink container. .. Thereby, the spillability of food and drink can be appropriately estimated according to the degree of influence of each of the above parameters on the spillability of food and drink. Therefore, the scattering index can be specified more appropriately.

Further, in the control device 100 according to the present embodiment, the susceptibility of food and drink to spill can be estimated based on at least one of each parameter of the viscosity of food and drink or the remaining amount of food and drink. Thereby, the spillability of food and drink can be appropriately estimated according to the degree of influence of each of the above parameters on the spillability of food and drink. Therefore, the scattering index can be specified more appropriately.

Further, in the control device 100 according to the present embodiment, the ease of eating and drinking of food and drink is estimated based on the food and drink information, and the scattering index can be specified according to the estimation result of the ease of eating and drinking of food and drink. Thereby, the scattering index can be appropriately specified according to the ease of eating and drinking of food and drink. Therefore, the possibility of scattering of food and drink can be more appropriately evaluated at each point of each route candidate according to the ease of eating and drinking of food and drink.

Further, in the control device 100 according to the present embodiment, the ease of eating and drinking of food and drink can be estimated based on at least one of each parameter of the temperature of food and drink or the presence or absence of an auxiliary tool for eating and drinking of food and drink. Thereby, it is possible to appropriately estimate the ease of eating and drinking of food and drink according to the degree of influence of each of the above parameters on the ease of eating and drinking of food and drink. Therefore, the scattering index can be specified more appropriately.

Further, in the control device 100 according to the present embodiment, the vehicle behavior is based on at least one of the clothes of the passengers in the vehicle, the number of passengers in the vehicle, the time zone to which the current time belongs, or each parameter of the destination of the vehicle. Thresholds for predictive information can be set. Thereby, the threshold value can be appropriately set according to each of the above parameters. Specifically, when it is more unfavorable for food and drink to scatter when eating and drinking due to each of the above parameters, the threshold value can be appropriately set.

Further, in the control device 100 according to the present embodiment, the threshold value can be adjusted based on the relationship between the comparison result between the vehicle behavior prediction information and the threshold value for the vehicle behavior prediction information and the actual behavior of food and drink. Thereby, the threshold value can be appropriately adjusted according to the actual behavior of the food and drink. Therefore, it is possible to improve the evaluation accuracy of the possibility of scattering of food and drink at each point of each route candidate.

Further, in the control device 100 according to the present embodiment, the recommended route can be determined based on the number of points where the vehicle behavior prediction information for each of the plurality of route candidates exceeds the threshold value. Thereby, the possibility of scattering of food and drink for each route candidate can be appropriately evaluated according to the number of points evaluated as having a relatively high possibility of scattering of food and drink. Therefore, it is more effectively realized that a route candidate capable of suppressing the scattering of food and drink in the vehicle is determined as a recommended route from a plurality of route candidates.

Further, in the control device 100 according to the present embodiment, the recommended route can be determined based on the size of the vehicle behavior prediction information with respect to the threshold value at each point for each of the plurality of route candidates. Thereby, the possibility of scattering of food and drink for each route candidate can be appropriately evaluated according to the degree of possibility of scattering of food and drink at each point in each route candidate. Therefore, it is more effectively realized that a route candidate capable of suppressing the scattering of food and drink in the vehicle is determined as a recommended route from a plurality of route candidates.

Further, in the control device 100 according to the present embodiment, a plurality of route candidates including the determined recommended route can be displayed by the display device. Thereby, the determined recommended route can be effectively presented to the driver.

Further, in the control device 100 according to the present embodiment, in a plurality of route candidates including the recommended route, an object indicating a point where the vehicle behavior prediction information exceeds the threshold value can be displayed by the display device. Thereby, it is possible to present to the driver the position of the point where the possibility of scattering of food and drink is relatively high. Therefore, it is possible to appropriately suppress the scattering of food and drink in the vehicle during traveling.

Further, in the control device 100 according to the present embodiment, the display mode of the object indicating the point where the vehicle behavior prediction information exceeds the threshold value can be changed according to the size of the vehicle behavior prediction information, and the object can be displayed by the display device. .. Thereby, in addition to the position of the point where the possibility of scattering of food and drink is evaluated to be relatively high, the degree of possibility of scattering of such a point can be presented to the driver. Therefore, it is possible to more appropriately suppress the scattering of food and drink in the vehicle during traveling.

Further, in the control device 100 according to the present embodiment, at least one of the dimensions, colors, or shapes of the objects indicating the points where the vehicle behavior prediction information exceeds the threshold value is made different according to the size of the vehicle behavior prediction information. The object can be displayed by the display device. Thereby, in addition to the position of the point where the possibility of scattering of food and drink is evaluated to be relatively high, it is effectively realized to present the driver with the degree of possibility of scattering of such a point.

Further, in the control device 100 according to the present embodiment, when there are a plurality of foods and drinks in the vehicle, the recommended route can be determined by preferentially using the smaller threshold value among the threshold values corresponding to each of the plurality of foods and drinks. As a result, it is possible to determine as a recommended route a route candidate that can more effectively suppress the scattering of food and drink in the vehicle from a plurality of route candidates.

Further, in the control device 100 according to the present embodiment, when there are a plurality of foods and drinks in the vehicle, the recommended route can be determined by preferentially using the threshold value corresponding to the foods and drinks eaten by the driver. Here, it is considered that it is difficult for the driver to stably grasp the food and drink as compared with other passengers. Therefore, by preferentially determining the recommended route by preferentially using the threshold value corresponding to the food and drink eaten by the driver, it is possible to suppress the food and drink from being scattered in the vehicle from among a plurality of route candidates. In addition, a route candidate having a relatively high compatibility with the priority conditions can be determined as a recommended route.

Further, in the control device 100 according to the present embodiment, the predicted value of the acceleration generated in the vehicle at each point in each of the plurality of route candidates can be predicted as the vehicle behavior prediction information. As a result, it is effectively realized that the possibility of scattering of food and drink is appropriately evaluated at each point of each route candidate.

<4. Conclusion>
As described above, in the control device 100 according to the present embodiment, a plurality of route candidates are obtained based on the food and drink information regarding the food and drink in the vehicle and the vehicle behavior prediction information regarding the vehicle behavior for each of the plurality of route candidates. The recommended route from the inside to the destination is decided. As a result, it is possible to appropriately determine a route candidate that can suppress the scattering of food and drink in the vehicle from a plurality of route candidates as a recommended route according to the unique property and state of the food and drink. Therefore, it is possible to present a recommended route that can suppress the scattering of food and drink in the vehicle.

In the above description, an example in which the upper camera 41, the side camera 42, the thermo camera 43, and the weight sensor 44 are provided as detection devices for detecting food and drink information in the control system 1 has been described, but the control system 1 is provided. The number and types of detectors are not limited to such examples. For example, some detection devices may be omitted from the configuration of the control system 1, the number of each detection device provided in the control system 1 may be different from the above-mentioned example, and the functions of some detection devices may be different. It may be realized by other devices.

Further, in the above description, an example in which the detection device provided in the control system 1 detects the food and drink information about the beverage D10 in the beverage container C10 housed in the cup holder H10 has been mainly described, but the food and drink information is mainly described by the detection device. The position of the food or drink in the vehicle in which is detected is not limited to such an example. For example, the detection device provided in the control system 1 may be able to detect food and drink information about food and drink located in a vehicle other than the cup holder H10. Thereby, it becomes possible to determine the recommended route based on the food / drink information regarding the food / drink located at another place in the vehicle different from the cup holder H10.

Further, the traveling state of the vehicle on which the control system 1 described above is mounted or when the process performed by the control device 100 is executed is not particularly limited. For example, when the control system 1 is mounted on a vehicle capable of executing an automatic driving mode that automatically travels without the driver's operation, the processing performed by the control device 100 described above is during execution of the automatic driving mode. May be executed in.

It should be noted that the processes described using the flowchart in the present specification do not necessarily have to be executed in the order shown in the flowchart. Some processing steps may be performed in parallel. Further, additional processing steps may be adopted, and some processing steps may be omitted.

Although the preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, the present invention is not limited to these examples. It is clear that a person having ordinary knowledge in the field of technology to which the present invention belongs can come up with various modifications or applications within the scope of the technical ideas described in the claims. , These are also naturally understood to belong to the technical scope of the present invention.

1 Control system 20 Navigation device 30 Communication device 41 Upper camera 42 Side camera 43 Thermo camera 44 Weight sensor 45 Vehicle speed sensor 46 Accelerometer 100 Control device 110 Acquisition unit 120 Prediction unit 130 Decision unit 131 Specific unit 131a First estimation unit 131b 2 Estimating unit 131c Index value specifying unit 132 Setting unit 133 Route determination unit 140 Control unit C10 Beverage container D10 Beverage H10 Cup holder

Claims (20)

A decision unit that determines a recommended route from the plurality of route candidates to the destination based on the food and drink information regarding the food and drink in the vehicle and the vehicle behavior prediction information regarding the behavior of the vehicle for each of the plurality of route candidates. To prepare
Control device. The decision unit
A specific unit that specifies a scattering index indicating the unique ease of scattering of the food or drink based on the food or drink information when eating or drinking.
A setting unit that sets a threshold value for the vehicle behavior prediction information according to the scattering index, and
A route determination unit that determines the recommended route based on the comparison result between the vehicle behavior prediction information and the threshold value for each of the plurality of route candidates.
To prepare
The control device according to claim 1. The specific unit estimates the spillability of the food and drink based on the food and drink information, and specifies the scattering index according to the estimation result of the spillability of the food and drink.
The control device according to claim 2. The specific unit estimates the susceptibility of the food and drink to spill based on the container information regarding the food and drink container.
The control device according to claim 3. The specific part estimates the susceptibility of the food or drink to spill based on the type of the food or drink container or at least one of the mouth widths of the food and drink container.
The control device according to claim 4. The specific part estimates the susceptibility of the food or drink to spill based on at least one of the viscosity of the food and drink or the remaining amount of the food and drink.
The control device according to any one of claims 3 to 5. The specific unit estimates the ease of eating and drinking of the food and drink based on the food and drink information, and specifies the scattering index according to the estimation result of the ease of eating and drinking of the food and drink.
The control device according to any one of claims 2 to 6. The specific unit estimates the ease of eating and drinking of the food and drink based on at least one of the temperature of the food and drink or the presence or absence of an auxiliary tool for eating and drinking the food and drink.
The control device according to claim 7. The setting unit sets the threshold value based on at least one of the clothes of the passenger in the vehicle, the number of passengers in the vehicle, the time zone to which the current time belongs, or the destination of the vehicle.
The control device according to any one of claims 2 to 8. The determination unit adjusts the threshold value based on the relationship between the comparison result between the vehicle behavior prediction information and the threshold value and the actual behavior of the food and drink.
The control device according to any one of claims 2 to 9. The route determination unit determines the recommended route based on the number of points where the vehicle behavior prediction information for each of the plurality of route candidates exceeds the threshold value.
The control device according to any one of claims 2 to 10. The route determination unit determines the recommended route based on the magnitude of the vehicle behavior prediction information with respect to the threshold value at each point for each of the plurality of route candidates.
The control device according to any one of claims 2 to 10. A control unit for displaying the plurality of route candidates including the determined recommended route on the display device is provided.
The control device according to claim 11 or 12. The control unit causes the display device to display an object indicating a point where the vehicle behavior prediction information exceeds the threshold value in the plurality of route candidates including the recommended route.
The control device according to claim 13. The control unit causes the display device to display the object by changing the display mode of the object according to the size of the vehicle behavior prediction information.
The control device according to claim 14. The control unit causes the display device to display the object by making at least one of the dimensions, colors, or shapes of the object different according to the size of the vehicle behavior prediction information.
The control device according to claim 15. When there are a plurality of the foods and drinks in the vehicle, the determination unit determines the recommended route by preferentially using the smaller threshold value among the threshold values corresponding to each of the plurality of foods and drinks.
The control device according to any one of claims 2 to 16. When there are a plurality of the foods and drinks in the vehicle, the determination unit determines the recommended route by preferentially using the threshold value corresponding to the foods and drinks eaten by the driver.
The control device according to any one of claims 2 to 16. It is provided with a prediction unit that predicts a predicted value of acceleration generated in the vehicle when traveling on each of the plurality of route candidates as the vehicle behavior prediction information.
The control device according to any one of claims 1 to 18. A detection device that detects food and drink information related to food and drink in the vehicle,
A control device including a determination unit that determines a recommended route from the plurality of route candidates to a destination based on the food and drink information and vehicle behavior prediction information regarding the behavior of the vehicle for each of the plurality of route candidates. ,
including,
Control system.

Images