JP6566999B2 - Operation control device, operation control method, and operation control program - Google Patents

Description

  Embodiments described herein relate generally to an operation control device, an operation control method, and an operation control program.

  Conventionally, genetic information obtained by genetic testing has been used in various fields. For example, Patent Document 1 discloses a technique for providing information that contributes to the health of a user using the result of the user's genetic analysis.

JP 2016-33795 A

  However, with the above-described conventional technology, it is not always possible to realize safer operation control for the user. For example, in the above-described conventional technology, the transmission target content searched using the target user's disease and disease factor specified from the target user's gene analysis result as a search key is transmitted to the target user.

  As described above, the above-described conventional technology specifies a disease related to the target user (a disease that may possibly take in the future) from the gene analysis result of the target user, and in order to grasp the disease so as not to suffer from this disease. The content related to is transmitted to the target user. Therefore, with the above-described conventional technology, it is not always possible to realize safer operation control for the user.

  The present application has been made in view of the above, and an object thereof is to realize operation control with higher safety for the user.

  The driving control device according to the present application includes an acquisition unit that acquires a user's genetic information, and safety when the user drives, based on the user's constitution predicted from the user's genetic information. And a control unit that performs predetermined control related to the operation of the user based on the indicated index.

  According to one aspect of the embodiment, there is an effect that it is possible to realize operation control with higher safety for the user.

FIG. 1 is a diagram illustrating an example of an operation control process according to the embodiment. FIG. 2 is a diagram illustrating a configuration example of the operation control system according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the operation control apparatus according to the embodiment. FIG. 4 is a diagram illustrating an example of the constitution information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of a gene information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of an operation history storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of the user constitution storage unit according to the embodiment. FIG. 8 is a diagram illustrating an example of the driving tendency storage unit according to the embodiment. FIG. 9 is a flowchart illustrating an operation control processing procedure by the operation control apparatus according to the embodiment. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the operation control apparatus.

  Hereinafter, modes for carrying out an operation control device, an operation control method, and an operation control program according to the present application (hereinafter referred to as “embodiments”) will be described with reference to the drawings. Note that the operation control device, the operation control method, and the operation control program according to the present application are not limited to the embodiment. Moreover, in the following embodiment, the same code | symbol is attached | subjected to the same site | part and the overlapping description is abbreviate | omitted.

[1. Operation control processing)
First, an example of the operation control process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of an operation control process according to the embodiment. The operation control process according to the embodiment is performed by the operation control apparatus 100 shown in FIG.

  In addition, the operation control system 1 according to the embodiment includes an automobile 10 and an operation control device 100 as shown in FIG. The automobile 10 and the operation control apparatus 100 are connected via a network so as to be communicable by wire or wirelessly. The operation control system 1 shown in FIG. 1 may include a plurality of automobiles 10 and a plurality of operation control devices 100. As will be described later, the operation control system 1 also includes an inspection organization server 30 and a management server 60.

  The automobile 10 is an automobile (also referred to as an autonomous driving vehicle) that can automatically run without human operation. The automobile 10 is equipped with a radar, a GPS (Global Positioning System), a camera, and the like. For example, the automobile 10 can autonomously travel to a designated destination by assisting the driving operation by the driver by recognizing the surrounding environment with a radar, GPS, camera, or the like. In addition, the automobile 10 provides driving guidance to the driver by a car navigation system using GPS (hereinafter, abbreviated as “car navigation”). For example, the automobile 10 displays a shortest route to a destination and a traffic congestion state acquired by car navigation on a display screen in the vehicle, and performs a driving guide such as guiding a driver to the destination using voice. In addition, from such a thing, it can be said that the automobile 10 is a terminal device used by a user. Further, the vehicle type of the automobile 10 according to the embodiment (ordinary automobile, light automobile, large automobile, etc.) is not limited.

  Note that the level of automation is set for the automobile 10 which is an autonomous driving car. Specifically, levels 0 to 4 are determined according to the level of automation. The higher the level value, the more automated it is. For example, the lowest level 0 indicates that all operations related to driving (acceleration, steering, braking) are entrusted to the driver. On the other hand, level 4 indicates fully automatic driving (the driver is not involved in driving). By the way, the automobile 10 according to the embodiment corresponds to level 3, and the system side performs all of acceleration, steering, and braking, but a driver's response is required as necessary in an emergency or the like.

  Next, the premise of this embodiment will be described. There may be a relationship between the constitution (character) of the user (driver) and dangerous driving that may lead to an accident (hereinafter sometimes simply referred to as “dangerous driving”). As a simple example, a user with low visual acuity may misrecognize the distance between the vehicle and the front vehicle and cause a collision accident.

  Here, visual loss is often caused by the lifestyle and age of the user (for example, the visual acuity gradually deteriorates due to daily long-term computer work), so the visual loss is considered so-called acquired. Can do. On the other hand, there is a so-called congenital constitution that is formed to some extent depending on the gene type.

  As an example, past genetic studies have shown that the constitution showing spatial recognition ability depends on the type of a particular gene. For example, such a gene is referred to as “GINE1” using a conceptual symbol for convenience of explanation. The gene “GINE1” has several types depending on the person. For example, among the genes “GINE1”, those who have the “α-type” gene “GINE1” have a spatial recognition ability higher than a predetermined reference value, and those who have the “β-type” gene “GINE1” There are innate constitutions (constitutions with a high contribution ratio of genes) formed according to the gene type, such that the spatial recognition ability is lower than a predetermined reference value.

  For example, it can be said that a person having a low spatial recognition ability tends to be unable to quickly recognize the state and relationship that an object occupies in a three-dimensional space. When such a person drives a car, for example, the consciousness concentrates on driving the car, which causes a misunderstanding of the surrounding environment, for example, the distance between the vehicle and the vehicle ahead, resulting in a traffic accident. May be caused.

  As described above, the operation control apparatus 100 according to the embodiment performs predetermined control related to driving the user's automobile 10 by using the presence of a constitution that is inherently determined by a gene. Specifically, the operation control apparatus 100 according to the embodiment acquires the genetic information of the user. Then, the driving control device 100 is a predetermined value related to the driving of the user based on an index indicating safety when the user is driving and indicating safety based on the constitution of the user predicted from the genetic information of the user. Control. Below, the operation control process concerning embodiment is demonstrated using an example.

  First, the driving control device 100 acquires a driving history that each user has driven the automobile 10 and genetic information of each user (step S1). The gene information is a test result obtained by the genetic test, for example, a list of genes that the user has. The gene information is information indicating the type (genotype) of a gene associated with the formation of a specific constitution (character) and the onset of a specific disease. Using the above example, assuming that a person whose “GINE1” type is a “β” type is known to have low spatial recognition ability, the user can know this by genetic testing. In addition, a person can know what kind of illness may develop in the future.

  The operation control apparatus 100 acquires such genetic information from the inspection organization that performed the genetic test. For example, the operation control apparatus 100 acquires gene information by accessing a server device (testing institution server 30) belonging to the testing institution. A predetermined contract is signed between the business owner who manages the operation control device 100 (hereinafter referred to as “business owner T”) and the testing institution, and the consent of the user who has undergone the genetic test is obtained. Such information can be obtained. However, the operation control apparatus 100 may receive gene information directly from a user who has undergone a genetic test. That is, there is no limitation on how the operation control apparatus 100 acquires gene information.

  Moreover, the operation control apparatus 100 stores the acquired gene information in the gene information storage unit 122 which is a storage unit in the own apparatus. The genetic information storage unit 122 is a storage unit that stores genetic information obtained by genetic testing. As shown in FIG. 1, identification information (user ID) for identifying each user who has undergone genetic testing, and the user Are stored in association with each other's gene information.

  That is, the gene information storage part 122 memorize | stores what type of gene each user has as gene information. For example, the gene information storage unit 122 stores what type of gene each user has among genes having a plurality of types by having SNP (Single Nucleotide Polymorphism) as gene information.

  For example, in the gene information storage unit 122 illustrated in FIG. 1, the gene information of the user U1 indicates that the user U1 has genes “G1a”, “G2c”, “G3a”, “G4a”,. Here, in this embodiment, the lower case alphabets assigned to the gene titles indicate the gene type. For example, the gene “G1a” indicates the “a” type gene “G1”. That is, it is indicated that the user U1 has the “a” type gene “G1” in the gene “G1” in which a plurality of types exist by SNP.

  Next, the driving history is history information obtained by accumulating driving modes indicating how each user has performed. Here, for example, the automobile 10 determines the driving mode of the user who is the driver based on the driving situation (for example, inter-vehicle distance, driving time, travel route) obtained from the radar, GPS, camera, etc., and the determined driving. The mode is transmitted to the management server 60 (not shown) every day of driving. As a result, the management server 60 accumulates the driving mode received from the automobile 10. Therefore, for example, the operation control apparatus 100 acquires an operation history from the management server 60 and stores the acquired operation history in the operation history storage unit 123.

  In the example of FIG. 1, the driving history storage unit 123 stores a driving history that is a driving mode determined from each user's driving status and indicates a driving mode for each day of driving. In the example of FIG. 1, an example is shown in which the user U1 has driven the automobile 10 at an average inter-vehicle distance of 3 m on “May 31, 2017”. In addition, an example is shown in which the average time during which the user U1 continuously operates on “May 31, 2017” is “10 minutes”. Further, an example is shown in which the number of times that the user U1 has selected the dangerous route during driving on “May 31, 2017” is “0”.

  Next, the operation control apparatus 100 specifies (predicts) the constitution of each user (step S2). In the example of FIG. 1, the operation control apparatus 100 shall identify the constitution of the users U1, U2, U3 (users U1 to U3). For example, the operation control apparatus 100 determines which type of which gene contributes to what kind of personality formation in the database (physical information storage unit 121 described later) stored for each gene type, and the user U1. The constitutional characteristics such as the constitution of each of the users U1 to U3 are specified by checking the genetic information of each of the U3.

  The constitution targeted in the present embodiment is assumed to be spatial recognition ability, spatial intelligence, and sleep depth as in the example of FIG. This is because these three constitutions have been found to be innate constitutions to which specific types of genes have contributed, so even if the constitution is unknown, even genetic information can be obtained from the user. This is because it can be predicted from the genetic information. Moreover, when these three constitutions have a high possibility of affecting a specific driving mode (for example, inter-vehicle distance, driving time, dangerous route selection situation) among driving modes such as how the user performs driving. This is because it is considered.

  The space recognition ability indicates the ability to quickly and accurately recognize the state and relationship (position, direction, posture, size, shape, interval, etc. of the object) that the object occupies in the three-dimensional space. Spatial intelligence indicates the ability to use video and spatial images. Specifically, spatial intelligence indicates the ability to perceive and memorize what position an object exists at what speed and relationship. Spatial intelligence is also related to the ability to judge things. The sleep depth is an index indicating the depth of sleep.

  In the example of FIG. 1, the operation control apparatus 100 has identified that the user U1 has the gene “G2c” (c-type gene G2), thereby having a constitutional feature such that the constitution “space recognition ability” is “low”. An example is shown. More simply, an example is shown in which the operation control apparatus 100 specifies that the user U1 has the gene “G2c” (c-type gene G2), thereby having a “low spatial recognition ability” constitution. In addition, an example is shown in which the operation control apparatus 100 specifies that the user U2 has the gene “G3c” (c-type gene G3), thereby having the constitutional feature such that the constitution “spatial intelligence” is “low”. In addition, an example is shown in which the operation control apparatus 100 specifies that the user U3 has the gene “G4c” (c-type gene G4), and thus the constitution “the depth of sleep” is a constitution feature such as “shallow”.

  In addition, the operation control apparatus 100 is based on the driving mode of each user stored in the driving history storage unit 123, such as the tendency of each user's driving mode, that is, what type of driving each user tends to perform. A driving tendency is specified (step S3). Note that, as described above, since the driving tendency is a tendency of the driving mode, “identifying the driving trend” can be rephrased as “acquiring the driving mode”.

  For example, the operation control apparatus 100 specifies that a user whose average inter-vehicle distance is equal to or smaller than a predetermined distance over a plurality of dates is a driving tendency “short inter-vehicle distance” (the inter-vehicle distance tends to be short). In addition, for example, the operation control apparatus 100 specifies that a user whose average continuous operation time is equal to or longer than a predetermined time over a plurality of dates is an operation tendency “continuous operation for a long time” (which tends to be continuous operation for a long time) . As an example, a person who commutes by car for more than one hour every day (without a break during this period) may be identified as a driving tendency “long-time continuous driving”. In addition, for example, in the operation control apparatus 100, a user whose total number of times of driving a dangerous route over a plurality of dates is a predetermined number or more is driving tendency “risk route selection” (has a tendency to drive a dangerous route). Is specified. The dangerous route is, for example, an area with many narrow roads, an area with many sharp curves, or an area with steep roads. Further, the method for specifying the driving tendency is merely an example, and the method for specifying the driving tendency is not limited.

  The example of FIG. 1 shows an example in which the driving control device 100 specifies “short-to-vehicle distance” as the driving tendency of the user U1 from the driving mode included in the driving history of the user U1. Moreover, the example which the driving | operation control apparatus 100 specified "long-time continuous driving | operation" as a driving | running tendency of the user U2 from the driving | operation aspect contained in the driving | operation history of the user U2 is shown. In addition, an example is shown in which the operation control apparatus 100 specifies “dangerous route selection” as the driving tendency of the user U3 from the driving mode included in the driving history of the user U3.

  In such a state, the driving control apparatus 100 determines how safe the users U1 to U3 drive the car 10 based on the constitution characteristics specified in step S2 and the driving tendency specified in step S3. The safety of driving such as whether to perform or not is evaluated (step S4). For example, the driving control device 100 calculates a safety evaluation value as an index indicating driving safety based on the constitutional characteristics and driving tendency. And the driving | operation control apparatus 100 evaluates the driving | operation safety | security of each of the users U1-U3 based on a safety evaluation value. A higher safety evaluation value indicates safer driving.

  How to calculate the safety evaluation value is not limited. An example is shown using the example of user U1. For example, the driving control device 100 multiplies an index value (physical index value to be described later) indicating the physical feature “space recognition ability: low” of the user U1 by an index value corresponding to the average inter-vehicle distance of the user U1. , Calculate the score. In other words, the driving control apparatus 100 calculates a score by multiplying the constitution feature index and the driving tendency index. The reason why attention is paid to the average inter-vehicle distance is that the user U1 has a driving tendency “distance between short vehicles”.

  Similarly, the operation control apparatus 100 calculates scores for the users U2 and U3. Here, assuming that the safety evaluation value is represented by a numerical value from 0 to 5, the operation control device 100 applies a predetermined correction to each score to reduce it to a numerical value from 0 to 5. The safety evaluation value is calculated. The higher the safety evaluation value, the higher the safety.

  For example, the driving control device 100 indicates that the lower the index value indicating the constitutional characteristics and the higher the risk of driving more dangerously, the higher the possibility of dangerous driving leading to an accident. Calculate the safety evaluation value. For example, the driving control device 100 calculates the safety evaluation value so that the value approaches 0 as the space recognition ability is low and the average inter-vehicle distance is short. The closer the value is to 0, the lower the safety and the higher the danger in driving. Further, the operation control device 100 calculates the safety evaluation value so that the value approaches 0 as the spatial intelligence is low and the average continuous operation time is long. In addition, the driving control device 100 calculates the safety evaluation value so that the value approaches 0 as the sleep depth is shallow and the number of times of selecting the dangerous route is larger.

  More specifically, the driving control apparatus 100 is configured such that, for one user, the index values indicating a plurality of constitutional characteristics are all low, and the values tend to approach zero as there are a plurality of tendency to drive more dangerously. Calculate the safety evaluation value.

  In the example of FIG. 1, the operation control apparatus 100 has a safety evaluation value “2.5” for the user U1, a safety evaluation value “2.0” for the user U2, and a safety evaluation value “2” for the user U3. .0 "is shown. For example, if it is determined in advance that a user whose safety evaluation value is lower than a predetermined value (for example, “3.0”) is determined to perform dangerous driving (driving safety is low), the driving control device 100 is The users U1 to U3 evaluate that the possibility of dangerous driving is high. That is, the driving control device 100 senses dangerous driving for the users U1 to U3 (step S5). In this example, the driving control device 100 senses dangerous driving for all of the users U1 to U3. This is because the driving control device 100 detects dangerous driving for the users U1 to U3 from among a number of users. Indicates that Therefore, there may be a user whose safety evaluation value is high and dangerous driving is not detected.

  Next, the driving control device 100 performs predetermined control related to the driving of the users U1 to U3 with respect to the users U1 to U3 who sensed the dangerous driving (step S6). In the above example, the driving control device 100 has detected a dangerous driving that can collide with another vehicle based on the fact that the user U1 has “low space recognition capability” and “the inter-vehicle distance tends to be short”. This is a danger perception caused by the fact that the user U1 easily misunderstands the inter-vehicle distance due to “low space recognition ability”. For this reason, for example, the operation control apparatus 100 proposes to drive the user U1 with the inter-vehicle distance opened by a predetermined value or more. For example, when the user U1 is driving the automobile 10, the operation control apparatus 100 controls the automobile 10 and displays proposal information for proposing to drive with an inter-vehicle distance greater than a predetermined value on a display screen in the vehicle. Let

  Further, in the above example, the operation control apparatus 100 performs dangerous driving with a high accident occurrence rate based on the fact that the user U2 has “low spatial intelligence” and “they tend to perform continuous operation for a long time”. I sensed. This is because the user U2 is easy to drive for a long time because the judgment ability is low due to “low spatial intelligence”, and therefore, the danger detection caused by accumulated driving fatigue. It is. For this reason, the operation control apparatus 100 proposes to avoid continuous operation for the user U2, for example. For example, when the user U2 is driving the automobile 10, the operation control apparatus 100 controls the automobile 10 to display proposal information for proposing to avoid continuous driving on the display screen in the vehicle.

  Further, in the above example, the driving control device 100 has detected a dangerous driving in which the accident occurrence rate is high based on the fact that the user U3 is “shallow sleep depth” and “there is a tendency to select a dangerous route”. . This is a risk perception caused by the fact that the user U3 is less able to select a dangerous route due to neglecting the safety route because the judgment ability is reduced because “the sleep depth is shallow”. For this reason, for example, the operation control device 100 proposes a detour route to detour the user U3 to a safer road. For example, when the user U3 is driving the automobile 10, the operation control apparatus 100 controls the automobile 10 to display proposal information for proposing a detour route on the display screen in the vehicle.

  Now, as explained so far, the operation control device 100 according to the embodiment acquires the genetic information of the user. Then, the driving control device 100 is a predetermined value related to the driving of the user based on an index indicating safety when the user is driving and indicating safety based on the constitution of the user predicted from the genetic information of the user. Control.

  In this way, the driving control device 100 can easily perform dangerous driving that leads to an accident by combining the user's constitution predicted from the gene information with the driving mode (driving tendency) of the user. This can be determined with high accuracy. For example, as described with reference to FIG. 1, if a user with a specific constitution tends to drive to cause an accident, it is caused by the constitution of the user, and driving that will lead to an accident in the future. It is possible to accurately determine that there is a high possibility that For this reason, since the operation control apparatus 100 can effectively make proposals for accident prevention and the like, it is possible to realize operation control with higher safety for the user.

[2. Operation control system configuration]
Next, the configuration of the operation control system according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram illustrating a configuration example of the operation control system 1 according to the embodiment. As shown in FIG. 2, the operation control system 1 includes an automobile 10, an inspection engine server 30, a management server 60, and an operation control device 100. The automobile 10, the inspection organization server 30, the management server 60, and the operation control device 100 are connected to be communicable via a network N by wire or wireless.

  Although omitted in FIG. 1, the operation control system 1 according to the embodiment further includes an inspection engine server 30 and a management server 60 as shown in FIG. 2. The inspection institution server 30 is a server device belonging to a predetermined inspection institution that performs a genetic test, and accumulates genetic information that is a test result. For example, the inspection organization server 30 transmits genetic information to the operation control device 100 in response to a request from the operation control device 100.

  The management server 60 accumulates information related to driving transmitted from each user's automobile 10 (for example, how each user has driven, how long the user has driven, etc.), Manage as operation history. In addition, when the operation control device 100 accesses, the management server 60 provides the operation history to the operation control device 100.

  Note that the management server 60 and the operation control device 100 may be configured as one server device. For example, the operation control apparatus 100 may have the function of the management server 60. In such a case, the driving control apparatus 100 acquires the driving mode from the automobile 10 and stores the acquired driving mode in the driving history storage unit 123, for example.

[3. Operation control device configuration]
Next, the operation control apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the operation control apparatus 100 according to the embodiment. As illustrated in FIG. 3, the operation control apparatus 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

(About the communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the automobile 10, the inspection organization server 30, and the management server 60, for example.

(About the storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 includes a constitution information storage unit 121, a gene information storage unit 122, a driving history storage unit 123, a user constitution storage unit 124, and a driving tendency storage unit 125.

(About the constitution information storage unit 121)
The constitution information storage unit 121 is a storage unit that stores genetic information such as which type of gene contributes to which constitution among genes and information indicating the relationship between the constitution characteristics. Which type of gene contributes to which constitution has been clarified by previous genetic studies. Here, FIG. 4 shows an example of the constitution information storage unit 121 according to the embodiment. In the example of FIG. 4, the constitution information storage unit 121 includes items such as “gene information”, “constitution”, “constitution characteristics”, and “constitution index value”.

  “Gene information” is information indicating genes that have a plurality of types by having SNPs, and that contribute to what characteristics the corresponding “constitution” expresses innately by humans. “Constitution” indicates an innate constitution caused by the corresponding “gene information”. “Constitution” indicates a constitution that may be a factor that causes a driving mode that leads to the occurrence of an accident. “Characteristic features” indicate what kind of features are actually formed due to “genetic information” corresponding to “physical features”. The “constitution index value” is information in which the “constitution characteristic” is quantified, and is an index value for indicating the constitution characteristic. Further, the “constitution index value” is used to calculate a safety evaluation value.

  That is, in the example of FIG. 4, the constitutional feature such as the constitution “spatial recognition ability” is “high” (for example, higher than a predetermined reference value) is congenitally formed by the gene “G2a” (c-type gene). An example is shown in which G2) contributes. Similarly, the gene “G2c” (c-type gene G2) contributes to the formation of a constitutional feature such as the constitution “spatial recognition ability” “low” (for example, lower than a predetermined reference value). An example is shown.

  Further, in the example of FIG. 4, an example is shown in which the constitutional feature value “3” obtained by quantifying this is associated with the constitution feature “high spatial recognition ability”. Further, an example is shown in which a constitutional index value “1” obtained by quantifying this is associated with a constitutional feature “low spatial recognition ability”.

(Regarding the gene information storage unit 122)
The genetic information storage unit 122 is a storage unit that stores genetic information obtained by genetic testing. Further, the gene information stored in the gene information storage unit 122 is acquired by the acquisition unit 131. Here, FIG. 5 shows an example of the gene information storage unit 122 according to the embodiment. In the example of FIG. 5, the gene information storage unit 122 includes items such as “user ID”, “gene information”, “name”, “address”, and “date of birth”.

  “User ID” indicates identification information for identifying a user. As described with reference to FIG. 1, “gene information” indicates gene information based on an analysis result obtained by analyzing a user's gene by genetic testing. Specifically, the gene information is information such as what type of gene each user has among genes having a plurality of types by having SNPs.

  “Name” indicates the name of the user. “Address” indicates the user's address. “Birth date” indicates the date of birth of the user. The “name”, “address”, and “birth date” are, for example, input by the user when applying for genetic testing.

  That is, in the example of FIG. 5, the user U1 has the gene “G1a” that is the gene “G1” of the “a” type among the genes “G1” having a plurality of types by SNP. Similarly, in the example of FIG. 5, the user U2 indicates that the gene “G1b” that is the gene “G1” of the “b” type among the genes “G1” having a plurality of types exists.

(About the driving history storage unit 123)
The driving history storage unit 123 is a storage unit that stores, for example, driving mode history information as information related to driving when each user drives the automobile 10. For example, the automobile 10 monitors a driving situation by a user with a radar, a GPS, a camera, and the like, and determines a driving mode for each day by the user. Then, the automobile 10 transmits the determined driving mode to the management server 60. For example, the automobile 10 determines (calculates) the driving mode such as the average speed, the average inter-vehicle distance, the average continuous driving time, and the driving route selection status on the day the user has driven based on the driving status. The determination is not performed on the day when the user is not driving. These driving modes are driving modes that may lead to an accident.

  The management server 60 stores the received driving mode as a driving history in the storage unit in the own device. The operation control apparatus 100 acquires an operation history from the management server 60 and stores it in the operation history storage unit 123. The management server 60 may determine the driving mode based on the driving situation monitored by the automobile 10.

  Here, FIG. 6 shows an example of the operation history storage unit 123 according to the embodiment. In the example of FIG. 6, the driving history storage unit 123 includes items such as “user ID”, “date”, “distance between vehicles”, “continuous driving time”, and “dangerous route selection count”. These items are examples, and any driving mode may be adopted as long as the driving mode is considered to be involved in the occurrence of an accident, for example.

  “User ID” indicates identification information for identifying a user. Such “user ID” is the same as that used in the gene information storage unit 122. “Date” indicates the date and time when the automobile 10 was driven by the user. The “inter-vehicle distance” indicates an average inter-vehicle distance that is an average of the inter-vehicle distance when the automobile 10 is driven on the corresponding “date”. The “continuous operation time” indicates an average continuous operation time that is an average of continuous operation times when the automobile 10 is driven on the corresponding “date”. “Dangerous route selection count” indicates the number of times that a dangerous route is selected (dangerous route passes) when the vehicle 10 is driven on the corresponding “date”.

  That is, the example of FIG. 6 shows an example in which the user U1 drives the automobile 10 with an average inter-vehicle distance of 3 m on “May 31, 2017”. In addition, an example is shown in which the average time during which the user U1 continuously operates on “May 31, 2017” is “10 minutes”. Further, an example is shown in which the number of times that the user U1 has selected the dangerous route during driving on “May 31, 2017” is “0”.

(User constitution storage unit 124)
The user constitution storage unit 124 stores information on the constitution of each user identified (predicted) by the identifying unit 132 described later. The user constitution storage unit 124 also stores constitution constitution index values and safety evaluation values of each user. FIG. 7 shows an example of the user constitution storage unit 124 according to the embodiment. In the example of FIG. 7, the user constitution storage unit 124 includes items such as “user ID”, “constitution”, “constitution feature”, “constitution index value”, and “safety evaluation value”.

  “User ID” indicates identification information for identifying a user. The “user ID” is the same as that used for the gene information storage unit 122 and the driving history storage unit 123. The “physical feature” indicates a feature of the “physical feature” predicted for the corresponding user. The “constitution index value” is an index value indicating a corresponding “constitution characteristic”. The “safety evaluation value” is an index value for evaluating safety when the corresponding user drives.

  That is, in the example of FIG. 7, an example in which the user U1 is predicted to have a constitution characteristic that the constitution “spatial recognition ability” is “low”, that is, the user U1 is predicted to have a “low” recognition ability “space recognition ability”. Indicates.

(About driving tendency storage unit 125)
The driving tendency storage unit 125 is a storage unit that stores the driving mode tendency (driving tendency) acquired for each user based on the driving mode of each user stored in the driving history storage unit 123. Here, FIG. 8 shows an example of the driving tendency storage unit 125 according to the embodiment. In the example of FIG. 8, the driving tendency storage unit 125 includes items such as “user ID” and “driving tendency”.

  “User ID” indicates identification information for identifying a user. The “user ID” is the same as that used for the gene information storage unit 122, the driving history storage unit 123, and the user constitution storage unit 124. The “driving tendency” indicates the tendency of the driving mode acquired for each user based on the driving mode of each user.

  That is, the example of FIG. 8 shows an example in which the driving tendency “distance between short cars” is acquired for the user U1. In other words, an example is shown in which it is specified that the user U1 “have a tendency to drive at a short inter-vehicle distance”.

(About the control unit 130)
Returning to FIG. 3, the control unit 130 executes various programs stored in a storage device inside the operation control apparatus 100 using a RAM as a work area by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Is realized. The control unit 130 is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As illustrated in FIG. 3, the control unit 130 includes an acquisition unit 131, a specification unit 132, an evaluation unit 133, an operation control unit 134, and a prediction unit 135, and includes information processing functions and Realize or execute the action. Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 3, and may be another configuration as long as the information processing described below is performed. Further, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship illustrated in FIG. 3, and may be another connection relationship.

(About the acquisition unit 131)
The acquisition unit 131 acquires the gene information of the user. For example, the acquisition unit 131 acquires genetic information from a testing organization that has performed a genetic test. For example, the acquisition unit 131 acquires genetic information by accessing the inspection organization server 30 that is a server device belonging to the inspection organization. For example, the acquisition unit 131 periodically accesses the inspection organization server 30 and acquires gene information. The acquisition unit 131 stores the acquired gene information in the gene information storage unit 122.

  The acquisition unit 131 further acquires the user's driving mode. Specifically, the acquisition unit 131 acquires a driving history including a driving mode when each user drives the automobile 10. For example, the acquisition unit 131 acquires the driving history by accessing the management server 60. For example, the acquisition unit 131 periodically accesses the management server 60 and acquires an operation history. Further, the acquisition unit 131 stores the acquired driving history in the driving history storage unit 123 as shown in FIG.

(About the specific part 132)
First, the process for identifying the constitution will be described. The identification unit 132 identifies (predicts) the user's constitution. Specifically, the specifying unit 132 specifies the constitution of the user for each user whose genetic information has been acquired by the acquiring unit 131 based on the user's genetic information. For example, the specifying unit 132 specifies, for each user, the characteristics (degrees) of spatial recognition ability, spatial intelligence, or sleep depth as the constitution.

  For example, it is assumed that the gene information of the users U1, U2, and U3 is acquired by the acquisition unit 131. In such a case, the specifying unit 132 compares the information in the constitution information storage unit 121 with the gene information of each of the users U1 to U3 stored in the gene information storage unit 122, and Identify constitutional features such as constitution. The specifying unit 132 acquires a constitution index value that is an index value of the identified constitution feature.

  In the example of FIG. 4, the gene involved in the constitution “spatial recognition ability” is the gene “G2”. In the example of FIG. 5, the user U1 has a “c-type” gene “G2”, that is, a gene “G2c”. Therefore, the specifying unit 132 predicts that the user U1 has a “low” constitution (constitution feature), and returns to FIG. 4 to acquire the constitution index value “1”.

  In the example of FIG. 4, the gene involved in the constitution “spatial intelligence” is the gene “G3”. In the example of FIG. 5, the user U2 has a “c-type” gene “G3”, that is, a gene “G3c”. Therefore, the specifying unit 132 predicts that the “spatial intelligence” has a “low” constitution (constitution feature), and returns to FIG. 4 to acquire the constitution index value “1”.

  In the example of FIG. 4, the gene involved in the constitution “sleep depth” is the gene “G4”. In the example of FIG. 5, the user U3 has a “c-type” gene “G4”, that is, a gene “G4c”. Therefore, the specifying unit 132 predicts that the “sleep depth” is the “shallow” constitution (constitution feature), and returns to FIG. 4 to acquire the constitution index value “1”. Then, the specifying unit 132 associates the constitution feature identified for each user with the constitution index value, and stores them in the user constitution storage unit 124.

  Next, a process for specifying a driving tendency will be described. The specifying unit 132 specifies a driving tendency that is a tendency of the user's driving mode. For example, the specifying unit 132 specifies the driving tendency of each user based on the driving mode of each user stored in the driving history storage unit 123. In the case of the above example, the specifying unit 132 specifies the driving tendency of the users U1 to U3 based on the driving modes of the users U1 to U3 from which the genetic information has been acquired.

  For example, the specifying unit 132 specifies that a user whose average inter-vehicle distance is equal to or less than a predetermined distance over a plurality of dates is a driving tendency “short inter-vehicle distance” (the inter-vehicle distance tends to be short). Further, for example, the specifying unit 132 specifies that a user whose average continuous operation time is equal to or longer than a predetermined time over a plurality of dates is an operation tendency “continuous operation for a long time” (which tends to be continuous operation for a long time). In addition, for example, the specifying unit 132 indicates that a user whose total number of times of driving a dangerous route over a plurality of dates is a predetermined number of times or more is driving tendency “risk route selection” (has a tendency to drive a dangerous route). Identify. Note that the specifying unit 132 is not limited to the above example, and the driving tendency may be specified using any method.

  In the example of the driving mode stored in the driving history storage unit 123 illustrated in FIG. 6, the user U1 is traveling at a very short inter-vehicle distance (3, 4 m) at any driving date and time. For this reason, the specific | specification part 132 specifies "distance between short cars" as a driving | running tendency of the user U1, for example. In addition, the user U2 performs continuous operation for a long time (60 minutes) at any operation date and time. For this reason, the specifying unit 132 specifies “long-time continuous operation” as the driving tendency of the user U2. Moreover, the user U3 has selected many dangerous routes at any driving date and time. For this reason, the specifying unit 132 specifies “dangerous route selection” as the driving tendency of the user U3, for example. Then, the specifying unit 132 stores the driving tendency specified for each user in the driving tendency storage unit 125.

(About the evaluation unit 133)
The evaluation unit 133 evaluates the driving safety such as how much the user performs a safe driving when driving the automobile 10 based on the constitution characteristic and the driving tendency specified by the specifying unit 132. For example, the evaluation unit 133 calculates a safety evaluation value as an index indicating driving safety based on the constitutional characteristics and driving tendency. Then, the evaluation unit 133 evaluates the driving safety of each user based on the safety evaluation value. The evaluation unit 133 may calculate the safety evaluation value using any method. Hereinafter, this example will be described.

  For example, the evaluation unit 133 calculates the score by multiplying the constitution index value and the driving tendency index value. The case of the user U1 will be described. Since the user U1 has the constitution feature “low space recognition ability”, the constitution evaluation value “1” is specified, and the driving tendency “short inter-vehicle distance” is specified. Has been. Therefore, the evaluation unit 133 calculates a score by multiplying the constitution evaluation value “1” by the index value corresponding to the driving tendency “distance between short vehicles”. The index value corresponding to the driving tendency “distance between short cars” is, for example, a value corresponding to an average value obtained by further averaging the average inter-vehicle distance on each driving day of the user U1.

  The case of the user U2 will be described. Since the user U2 has the constitution feature “low spatial intelligence”, the constitution evaluation value “1” is specified, and the driving tendency is “long-time continuous operation”. Have been identified. Therefore, the evaluation unit 133 calculates a score by multiplying the constitution evaluation value “1” by the index value corresponding to the driving tendency “long-time continuous operation”. The index value corresponding to the driving tendency “long-time continuous driving” is, for example, a value corresponding to an average value obtained by further averaging the average continuous driving time on each driving day of the user U2.

  The case of the user U3 will be described. Since the user U3 has the constitution characteristic “the depth of sleep is shallow”, the constitution evaluation value “1” is specified, and the driving tendency “dangerous route selection” is specified. ing. For this reason, the evaluation unit 133 calculates a score by multiplying the constitution evaluation value “1” by the index value corresponding to the driving tendency “dangerous route selection”. The index value according to the driving tendency “dangerous route selection” is, for example, a value according to an average value obtained by averaging the number of times of dangerous route selection on each driving day of the user U3.

  Then, the evaluation unit 133 calculates a safety evaluation value based on the score calculated as described above. Moreover, the evaluation part 133 is good also considering the score itself as a safety evaluation value. Further, if the safety evaluation value is expressed by a numerical value from 0 to 5, the evaluation value 133 is reduced to a numerical value from 0 to 5 by applying a predetermined correction to the score. Calculate the value.

  For example, the evaluation unit 133 indicates that the lower the index value indicating the constitutional feature and the higher the risk of driving more dangerously, the higher the possibility of dangerous driving leading to an accident. Calculate the safety evaluation value. For example, the driving control device 100 calculates the safety evaluation value so that the value approaches 0 as the space recognition capability is low and the inter-vehicle distance is short. The closer the value is to 0, the lower the safety and the higher the danger in driving. The operation control device 100 calculates the safety evaluation value so that the value approaches 0 as the spatial intelligence is low and the continuous operation time is long. In addition, the driving control device 100 calculates the safety evaluation value so that the value approaches 0 as the sleep depth is shallow and the number of times of selecting the dangerous route is larger.

  In addition, the evaluation unit 133 stores the safety evaluation value calculated by each user in the user constitution storage unit 124. In the example of FIG. 7, the evaluation unit 133 has a safety evaluation value “2.5” for the user U1, a safety evaluation value “2.0” for the user U2, and a safety evaluation value “2. The example which calculated "0" is shown. For example, it is assumed that a user whose safety evaluation value is lower than “3.0” is determined in advance to determine that dangerous driving is performed (driving safety is low). In such a case, the evaluation unit 133 evaluates that the users U1 to U3 are likely to perform dangerous driving. That is, the evaluation unit 133 senses dangerous driving for the users U1 to U3.

  For example, the evaluation unit 133 detects that the user U1 has a high possibility of causing a traffic accident in the future due to the fact that the distance between the vehicles is easily misunderstood due to “low space recognition capability”. Further, if the evaluation unit 133 is the user U2, it is easy to drive for a long time because the judgment ability is reduced due to “low spatial intelligence”, and therefore driving fatigue is accumulated. I understand that there is a high possibility of future traffic accidents. In addition, if the evaluation unit 133 is the user U3, it is easy to select a risky route due to neglecting the safety route because the judgment ability is reduced because the sleep depth is shallow. Sense that there is a high possibility of causing traffic accidents.

  Here, although not shown, the user U4 has “low spatial recognition ability” and “low spatial intelligence”. Furthermore, it is assumed that the user U4 has a tendency of “short-to-vehicle distance” and “long-time continuous operation”. In such a case, the evaluation unit 133 calculates a safety evaluation value so that the value is lower than that of the users U1 to U3. In other words, the evaluation unit 133 calculates the safety evaluation value so that the value becomes lower as the constitution leading to the dangerous driving is shown more and the driving tendency that may cause the accident is shown more.

(About the operation control unit 134)
The driving control unit 134 is a predetermined control related to the driving of the user based on an index indicating safety when the user is driving and indicating safety based on the constitution of the user predicted from the genetic information of the user. I do. Specifically, the driving control unit 134 performs predetermined control based on an index based on the user's constitution predicted from the user's genetic information and the driving mode of the user. For example, the driving control unit 134 is calculated based on a constitution index value corresponding to the constitution of the user predicted from the genetic information of the user and an index value corresponding to a tendency (driving tendency) of the user's driving mode. Predetermined control is performed based on the safety index value. For example, the driving control unit 134 performs predetermined control when it is determined that the user may perform dangerous driving based on the safety index value.

  The operation control unit 134 performs predetermined control at a timing based on the user's driving situation and the user's driving mode. For example, the operation control unit 134 performs predetermined control at a timing at which the user does not drive in such a driving mode based on the user's driving situation and the user's driving mode.

  Moreover, the driving | operation control part 134 performs the proposal based on a user's driving | operation aspect with respect to a user as predetermined | prescribed control. For example, the driving control unit 134 proposes not to drive in the driving mode indicated by the driving tendency of the user, or proposes a safe driving mode instead of this driving mode. Further, for example, the operation control unit 134 performs route guidance based on the driving mode indicated by the user's driving tendency as the predetermined control. The above operation control is demonstrated using the example of the users U1-U3.

  As described above, the evaluation unit 133 has detected a dangerous driving that can collide with another vehicle based on the fact that the user U1 has “low space recognition ability” and “the inter-vehicle distance tends to be short”. This is a danger perception caused by the fact that the user U1 easily misunderstands the inter-vehicle distance due to “low space recognition ability”. For this reason, the driving control unit 134 suggests, for example, that the user U1 drives the vehicle with the inter-vehicle distance opened by a predetermined value or more. For example, when the user U1 is driving the automobile 10, the operation control unit 134 controls the automobile 10 and displays proposal information that suggests driving with an inter-vehicle distance greater than a predetermined value on a display screen in the vehicle. Let

  Here, even if the user U1 travels at a predetermined speed or more and makes a proposal after the distance between the vehicles is made shorter than a predetermined distance (for example, a short distance that directly leads to an accident), the user suddenly May not be available. Therefore, in order to prevent an accident, it is more effective to make a proposal before such a driving situation occurs. Therefore, for example, when the user U1 is traveling at a predetermined speed or more, the operation control unit 134 controls the automobile 10 so that the inter-vehicle distance is shorter than a predetermined distance (for example, a short distance that is directly connected to an accident). Before, the proposal information regarding the inter-vehicle distance is displayed.

  As another example, the operation control unit 134 may control the automobile 10 to display the proposal information related to the inter-vehicle distance when the user U1 starts the engine of the automobile 10. The driving control unit 134 may control the automobile 10 to output the proposal information by voice.

  As described above, the operation control apparatus 100 according to the embodiment makes a proposal based on the user's operation mode before the operation mode is changed. Thereby, since the operation control apparatus 100 can make a user aware, it can prevent the occurrence of an accident.

  In addition, as described above, the evaluation unit 133 detects a dangerous driving with a high accident occurrence rate based on the fact that the user U2 has “low spatial intelligence” and “have a tendency to perform continuous driving for a long time”. did. This is because the user U2 is easy to drive for a long time because the judgment ability is low due to “low spatial intelligence”, and therefore, the danger detection caused by accumulated driving fatigue. It is. For this reason, the operation control unit 134 proposes, for example, to avoid continuous operation to the user U2. For example, when the user U2 is driving the automobile 10, the operation control apparatus 100 controls the automobile 10 to display proposal information for proposing to avoid continuous driving on the display screen in the vehicle.

  Here, even if the proposal is made after the user U2 has operated continuously for a long time, the effect of the proposal is weak. Therefore, in order to prevent an accident, it is more effective to make a proposal before such a driving situation occurs. Therefore, for example, the operation control unit 134 controls the automobile 10 to display the proposal information regarding the continuous operation before the user U2 enters the continuous operation for a long time. For example, the operation control unit 134 controls the automobile 10 to display the proposal information immediately before a time (for example, continuous operation for one hour) that is considered to start to accumulate fatigue due to continuous operation elapses.

  As another example, the operation control unit 134 may control the automobile 10 to display the proposal information regarding the continuous operation when the user U2 starts the engine of the automobile 10. The driving control unit 134 may control the automobile 10 to output the proposal information by voice.

  As described above, the operation control apparatus 100 according to the embodiment makes a proposal based on the user's operation mode before the operation mode is changed. Thereby, since the operation control apparatus 100 can make a user aware, it can prevent the occurrence of an accident.

  Further, as described above, the evaluation unit 133 has detected a dangerous driving in which the accident occurrence rate is high based on the fact that the user U3 is “shallow sleep depth” and “there is a tendency to select a dangerous route”. This is a risk perception caused by the fact that the user U3 is less able to select a dangerous route due to neglecting the safety route because the judgment ability is reduced because “the sleep depth is shallow”. Therefore, for example, the operation control unit 134 proposes a detour route to detour the user U3 to a safer road. For example, when the user U3 is driving the automobile 10, the operation control unit 134 controls the automobile 10 and displays proposal information for proposing a detour route on the display screen in the vehicle.

  Here, even if the user U3 actually makes a proposal while traveling on a dangerous route, the effect of the proposal is weak. Therefore, in order to prevent accidents due to dangerous route driving, it is more effective to make a proposal before such a driving situation occurs. Therefore, for example, the driving control unit 134 controls the automobile 10 to display the proposal information related to the dangerous route when it is found that the user U3 enters the dangerous route. For example, the operation control unit 134 controls the automobile 10 to display the proposal information when the distance between the automobile 10 of the user U3 and the area where the dangerous route exists is equal to or less than a predetermined distance.

  As another example, the operation control unit 134 may control the automobile 10 to display the proposal information related to the dangerous route when the user U3 starts the engine of the automobile 10. The driving control unit 134 may control the automobile 10 to output the proposal information by voice.

  As described above, the operation control apparatus 100 according to the embodiment makes a proposal based on the user's operation mode before the operation mode is changed. Thereby, since the operation control apparatus 100 can make a user aware, it can prevent the occurrence of an accident.

(About the prediction unit 135)
Now, so far, the driving control apparatus 100 acquires the user's genetic information and driving tendency, and performs driving control when detecting dangerous driving based on the acquired genetic information and driving tendency, and shows an example. It was. However, the operation control apparatus 100 may not be able to acquire the user's driving tendency because the user's driving history cannot be acquired. For example, the acquisition unit 131 cannot acquire a driving history from a user who has just purchased the automobile 10 and has hardly driven it. Moreover, although the acquisition part 131 has the motor vehicle 10, it cannot acquire a driving history from the user who is a paper driver. And about the user who cannot acquire a driving history in this way, the specific | specification part 132 cannot acquire a driving | operation aspect.

  Therefore, the driving control apparatus 100 may perform a process of predicting the driving mode for a user who cannot acquire the driving mode. For example, the prediction unit 135 predicts the driving mode for a user who cannot acquire the driving mode (referred to as “unknown user”). Specifically, the prediction unit 135 determines the relationship between the genetic information of another user who is a different user from the unknown user (referred to as “other user”) and the driving mode of the other user, and the unknown user's Based on the gene information, the driving mode of the unknown user is predicted. For example, the prediction unit 135 predicts a driving tendency of an unknown user. For example, the prediction unit 135 can generate a model by machine learning, and can predict a driving tendency of an unknown user using the generated model.

  Here, the unknown user is a user whose driving tendency is unknown because the genetic information is acquired but the driving history cannot be acquired. On the other hand, another user is a user whose driving tendency is specified by acquiring genetic information and also acquiring a driving history.

  In such a state, the prediction unit 135 analyzes the relationship between the driving tendency of other users and the genetic information. Specifically, the prediction unit 135 analyzes whether or not a predetermined relationship is established between the driving tendency and the genetic information. For example, the prediction unit 135 analyzes whether or not the relationship based on the tendency between the driving tendency and the gene information is established as the predetermined relationship. More specifically, the prediction unit 135 analyzes the correlation between the driving tendency of a plurality of other users and the gene information of the plurality of other users.

  For example, the prediction unit 135 correlates driving tendency and gene information using any machine learning technique such as a learning device using GWAS (Genome-Wide Association Study), deep learning, SVM (Support Vector Machine), etc. Analytical techniques such as learning sex can be used. Here, an example of machine learning will be described.

  For example, the prediction unit 135 generates a model for each driving tendency stored in the driving tendency storage unit 125. For example, the predicting unit 135 learns the relationship between the gene information of a plurality of other users and the driving tendency of the plurality of other users, and what kind of driving tendency the other users having each gene information have. Then, a model showing the correlation between each gene information and driving tendency is generated. For example, the prediction unit 135 is a model that is generated based on gene information and driving tendency, and can predict whether an unknown user is in a specific driving tendency when genetic information of an unknown user is input. Generate a model that outputs index values.

  That is, the prediction unit 135 sets the result information indicating whether or not the vehicle has a predetermined driving tendency as an objective variable in machine learning. And the prediction part 135 makes the genetic information (feature information) which the other user who exists in the said predetermined driving | operation tendency has as an explanatory variable in machine learning. And the prediction part 135 produces | generates the model regarding a predetermined driving | running tendency using an objective variable and an explanatory variable.

  For example, the prediction unit 135 generates an expression indicating the relationship between whether or not the unknown user has a predetermined driving tendency and the genetic information of other users who have the predetermined driving tendency. Furthermore, the prediction unit 135 calculates what weight each gene information has for an event that an unknown user has a predetermined driving tendency. Thereby, the prediction unit 135 can obtain information such as how much each gene information contributes to an event that an unknown user has a predetermined driving tendency. For example, the prediction unit 135 creates the following equation (1) when generating a model indicating the driving tendency “distance between short cars” among the driving tendency stored in FIG. 8. For convenience of explanation, “short-to-vehicle distance” driving is assumed to be “D1”.

  As shown in the following formula (1), the model generated by the prediction unit 135 includes an input layer to which genetic information of an unknown user is input, an output layer, and any layer from the input layer to the output layer. A first element belonging to a layer other than the output layer, and a second element whose value is calculated based on the first element and the weight of the first element, and according to genetic information input to the input layer Thus, this is a model for causing a computer to function so as to output a value obtained by quantifying the driving mode of the subject from the output layer.

y _(D1) = ω ₁ · x ₁ + ω ₂ · x ₂ + ω ₃ · x ₃ ... + ω _N · x _N (1) (N is an arbitrary number)

In the above formula (1), “y _(D1) ” indicates an event “whether or not the unknown user has a tendency of“ distance between short cars ”.” “Y _(D1) ” corresponds to the second element.

In the above formula (1), “x” corresponds to each explanatory variable of another user and also corresponds to the first element. In addition, the explanatory variable “x ₁ , x ₂ , x ₃ ,..., X _N ” in the above formula (1) is the “variable between short wheels” among the genes stored in the gene information storage unit 122 shown in FIG. The genetic information of other users determined to have a tendency of “distance” is used as an explanatory variable. For example, “x ₁ ” indicates a variable corresponding to the gene “G1a”.

In the above formula (1), “ω” is a weighting factor of “x”, and corresponds to the weight of the first element. Specifically, “ω ₁ ” is a weighting factor of “x ₁ ”, “ω ₂ ” is a weighting factor of “x ₂ ”, and “ω ₃ ” is a weighting factor of “x ₃ ”. It is. In this way, the above equation (1) is a variable including the explanatory variable “x” and the weighting coefficient “ω” corresponding to each of the genetic information of other users when there is a tendency of “distance between short cars” (for example, “ ω ₁ · x _{1 ”} )).

The above formula (1) will be described more specifically. In the above formula (1), “x ₁ ” corresponds to the gene “G1a”, “x ₂ ” corresponds to the gene “G1b”, and “x ₃ ” is an explanatory variable corresponding to the gene “G2a”. Suppose there is. In this case, in the example of FIG. 8, the above formula (1) corresponding to the user U1 who tends to be “short-to-short distance” can be expressed as the following formula (2).

y _(D1) (= 1) = ω ₁ · (G1a) + ω ₂ · (G1b = 0) + ω ₃ · (G2a = 0) + ω _N · x _N (2)

  Based on the example of FIG. 8, since the user U1 does not have the gene “G1b” or the gene “G2a”, “0” is applied to them as shown in the above formula (2).

  Based on the same idea, a formula can be created for each of the other users who are determined to have a driving tendency “distance between short cars”.

  The prediction unit 135 generates an expression for each other user as in the above expression (2), and uses the generated expression as a machine learning sample. Then, the prediction unit 135 derives a value corresponding to the weighting coefficient “ω” by performing an arithmetic processing of a sample expression. That is, the predicting unit 135 determines a weighting coefficient “ω” that satisfies the above equation (2). In other words, the prediction unit 135 can determine the weighting coefficient “ω” indicating the influence of the predetermined explanatory variable on the objective variable “y”. For example, if the gene “G2c” contributes significantly compared to other variables as information indicating the characteristics of the user in the driving tendency “distance between short cars”, the weight coefficient corresponding to the gene “G2c” A large value is calculated as compared with other variables.

  As described above, the predicting unit 135 generates a model that associates an event of a tendency of sudden braking operation with gene information acquired from a user who has a tendency of sudden braking operation. In the calculation process using the above equation (1), the left side is not set to “1” or “0” itself, but a predetermined error is assumed, and a value obtained by squaring the difference from the error is a minimum value. The optimal solution of “ω” may be calculated using a method such as a least square method that approximates to

  Note that the prediction unit 135 stores each model generated as described above in a predetermined storage unit. Although not shown, for example, the prediction unit 135 stores each model in a learning model storage unit which is a dedicated storage unit.

  In this way, the prediction unit 135 generates a model in advance. Here, it is assumed that the prediction unit 135 receives information indicating that the operation history of the user Ux cannot be acquired from the acquisition unit 131. In such a case, the prediction unit 135 predicts the driving tendency of the user Ux based on the result output from the model by inputting the genetic information of the user Ux to the model. For example, the predicting unit 135 outputs the score of the user Ux by inputting the gene information of the user Ux stored in the gene information storage unit 122 into the model. Then, when there is a model whose output score is equal to or greater than a predetermined value among the models, the prediction unit 135 predicts a driving tendency as an objective variable for the model as a driving tendency of the user Ux.

  The driving control unit 134 performs the predetermined control described so far based on an index based on the constitution of the user Ux predicted from the genetic information of the user Ux and the driving tendency predicted for the user Ux.

[4. Processing procedure)
Next, the procedure of the operation control process executed by the operation control apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 9 is a flowchart illustrating an operation control processing procedure performed by the operation control apparatus 100 according to the embodiment.

  First, the operation control apparatus 100 acquires a user's driving history and the user's genetic information (step S101). For example, the driving control apparatus 100 acquires a driving history from the management server 60 that manages information related to driving transmitted from the automobile 10. The driving history includes a driving mode when the automobile 10 is driven by each user. For example, the operation control apparatus 100 acquires the genetic information of each user from the inspection organization server 30.

  Next, the driving control device 100 specifies a constitutional feature that is a feature of the user's constitution based on the genetic information of the user for each user from whom the genetic information has been acquired (step S102). For example, the driving control device 100 specifies a constitutional feature indicating a degree of spatial recognition ability, spatial intelligence, or sleep depth. Moreover, the driving | operation control apparatus 100 specifies a user's driving | running tendency based on a user's driving | operation aspect (step S103). Note that the order in which the process in step S102 and the process in step S103 are performed may be reversed, and the process in step S102 and the process in step S103 may be performed simultaneously.

  Next, the driving control device 100 calculates a safety evaluation value indicating the safety when the user drives the automobile 10 based on the constitutional characteristics and driving tendency (step S104). And the driving | operation control apparatus 100 performs predetermined | prescribed control regarding driving | operation according to the evaluation result based on a safety evaluation value (step S105). For example, when the safety evaluation value is lower than a predetermined reference value, the operation control apparatus 100 detects that the target user may perform dangerous driving that leads to an accident that is caused by his / her driving tendency. For this reason, the operation control apparatus 100 performs control based on the driving tendency as predetermined control related to driving. For example, the driving control device 100 controls the automobile 10 to perform the safety guide at a timing before the target user drives in the driving mode indicated by the driving tendency.

[5. (Modification)
The operation control apparatus 100 according to the above embodiment may be implemented in various different forms other than the above embodiment. Therefore, in the following, another embodiment of the operation control apparatus 100 will be described.

[5-1. (Route guidance)
In the said Example, when the driving | operation control part 134 detected dangerous driving | running | working, the example based on a user's driving | running | working aspect was shown. However, without being limited to this example, the driving control unit 134 may perform route guidance based on the driving mode of the user when a dangerous driving is detected. As in the above example, when the user U1 has a tendency of “distance between short cars” due to “low space recognition ability”, the driving control unit 134 can travel relatively safely without worrying about the distance between the cars ( For example, the vehicle 10 is controlled so as to propose a route with a low traffic volume, a route with a small signal, a route with a large number of straight lines that are maintained. For example, when the user U1 uses the car navigation system to set the guidance to the destination with respect to the automobile 10, the operation control unit 134 uses the safety as described above as one of the candidate routes to the destination. Control to propose a travel route.

  Further, as in the above example, when the user U2 has a tendency of “continuous operation for a long time” because of “low spatial intelligence”, the operation control unit 134, for example, selects a route where “road station” exists. The vehicle 10 is controlled as proposed. For example, when the user U1 uses the car navigation system to set the guidance to the destination with respect to the automobile 10, the operation control unit 134 uses the safety as described above as one of the candidate routes to the destination. Control to propose a travel route.

  Further, as in the above example, when the user U3 has a tendency of “risk route selection” because “the sleep depth is shallow”, the driving control unit 134 can travel relatively safely without worrying about the inter-vehicle distance. The automobile 10 is controlled so as to propose the above. For example, when the user U1 uses the car navigation system to set the guidance to the destination with respect to the automobile 10, the operation control unit 134 uses the safety as described above as one of the candidate routes to the destination. Control to propose a travel route.

  Thereby, since the driving | running control apparatus 100 concerning embodiment can avoid the dangerous driving | running | working by a user, it can prevent an accident.

[5-2. Automatic operation control)
In the said Example, when the driving | operation control part 134 detected dangerous driving | running | working, the example based on a user's driving | running | working aspect was shown. However, not limited to this example, the driving control unit 134 may perform driving control on the automobile 10 that is a moving body driven by the user, based on the driving mode of the user. As described above, the automobile 10 is an autonomous driving vehicle that can automatically run. Therefore, the driving control here indicates that the automobile 10 is controlled to automatically adjust the driving regardless of the driving operation (for example, accelerator operation, brake operation, steering wheel operation) by the user.

  As in the above example, when the user U1 has a tendency of “distance between short cars” due to “low space recognition ability”, the driving control unit 134 moves the front vehicle while traveling at a predetermined speed or higher with respect to the automobile 10. When it is predicted that the inter-vehicle distance will be shorter than the predetermined distance, control is performed so as to increase the inter-vehicle distance by applying a brake regardless of the driving operation of the user U1.

  Thereby, since the driving | running control apparatus 100 concerning embodiment can avoid the dangerous driving | running | working by a user, it can prevent an accident.

  The driving control unit 134 controls the vehicle 10 to apply a brake and adjust the speed based on the distance relationship between the pedestrian or the motorcycle and the vehicle 10 when the vehicle 10 finds a pedestrian or a motorcycle. May be.

[5-3. (Considering accident history further)
In addition, when it is determined that the user's constitution is involved in the occurrence of the accident based on the user's constitution and the user's accident history, the operation control unit 134 performs the control related to the driving described above. May be. In such a case, for example, the acquisition unit 131 also acquires the accident history of the user who acquired the genetic information. In general, it is the insurance company that has an accident history. Therefore, the acquisition unit 131 acquires accident history data indicating the accident history of the user who acquired the genetic information from, for example, an insurance company that has a predetermined contract with the business owner T. Next, the specifying unit 132 predicts the user's constitution characteristic based on the user's genetic information as described above. Further, the specifying unit 132 refers to the accident history data of the user who has predicted the constitution characteristic and determines whether or not the constitution characteristic is involved in the occurrence of the accident.

  For example, the specifying unit 132 refers to the accident history data of each user who is predicted to have a constitution characteristic “low spatial recognition ability”, and whether or not the user who has predicted the constitution characteristic “low spatial recognition ability” is likely to cause an accident. Determine. For example, the specifying unit 132 calculates the probability (accident rate) that the user who has predicted the constitution characteristic “low spatial recognition ability” causes an accident (an accident rate), and if the accident rate is equal to or higher than a predetermined value, the constitution A user who has predicted that the feature “low spatial recognition ability” is likely to cause an accident. Further, by determining in this way, the specifying unit 132 determines that the constitutional feature “low spatial recognition ability” is involved in the occurrence of the accident. Here, the constitutional feature “low spatial recognition ability” has been described as an example, but the specifying unit 132 similarly determines whether or not other constitutional features are involved in the occurrence of the accident. .

  Next, the evaluation unit 133 evaluates safety by calculating a safety evaluation value for a user having a constitutional characteristic determined to be involved in the occurrence of the accident by the specifying unit 132. In addition, when the evaluation unit 133 detects dangerous driving, the driving control unit 134 performs control related to driving.

  Thereby, since the driving control device 100 according to the embodiment can accurately identify a user having a constitution that is involved in the occurrence of an accident, by performing suggestions and driving control for such a user, Generation | occurrence | production can be prevented effectively.

[5-4. (Use only constitution)
In the said Example, the operation control part 134 showed the example which performs control regarding a driving | operation based on a user's constitution and a driving | operation aspect. However, the driving control unit 134 may perform driving-related control using only the user's constitution. In such a case, the acquisition unit 131 may acquire only the user's genetic information and not acquire the driving history. Then, when the user's constitution characteristic is specified from the user's genetic information by the specifying unit 132, the evaluation unit 133 evaluates the safety only with this constitution characteristic. For example, when the constitutional feature of the user U1 is “low spatial intelligence”, the evaluation unit 133 evaluates that the safety when the user U1 drives is low (the possibility of dangerous driving is high). In addition, the operation control unit 134 performs control as described above for the user U1.

  As described above, the driving control apparatus 100 according to the embodiment can detect dangerous driving and perform control to avoid it without using the driving history of the user. In addition, information suggestion based on the constitution can be made even for a user who uses a general passenger car equipped with only a car navigation system.

[6. Hardware configuration)
Further, the operation control apparatus 100 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in FIG. 10, for example. FIG. 10 is a hardware configuration diagram illustrating an example of a computer 1000 that realizes the functions of the operation control apparatus 100. The computer 1000 includes a CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. The communication interface 1500 receives data from other devices via the communication network 50 and sends the data to the CPU 1100, and transmits the data generated by the CPU 1100 to other devices via the communication network 50.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. Further, the CPU 1100 outputs the generated data to the output device via the input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the operation control apparatus 100 according to the embodiment, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. The HDD 1400 stores data in the storage unit 120. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from other devices via the communication network 50.

[7. Others]
Of the processes described in the above embodiment, all or part of the processes described as being automatically performed can be performed manually, or all of the processes described as being performed manually or A part can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  Moreover, each embodiment mentioned above can be combined suitably in the range which does not contradict a process content.

[8. effect〕
The operation control apparatus 100 according to the embodiment includes an acquisition unit 131 and an operation control unit 134. The acquisition unit 131 acquires the gene information of the user. The driving control unit 134 is a predetermined control related to the driving of the user based on an index indicating safety when the user is driving and indicating safety based on the constitution of the user predicted from the genetic information of the user. I do.

  Thereby, the operation control apparatus 100 according to the embodiment can realize operation control with higher safety for a user who easily performs dangerous driving.

  Further, the acquisition unit 131 further acquires the user's driving mode, and the driving control unit 134 is predetermined based on an index based on the user's constitution predicted from the user's genetic information and the user's driving mode. Control.

  As a result, the operation control apparatus 100 according to the embodiment can accurately determine whether or not the user is likely to perform dangerous driving that leads to an accident, and thus is safer for a user who is likely to perform dangerous driving. Operation control can be realized.

  The operation control apparatus 100 according to the embodiment further includes a prediction unit 135. The prediction unit 135 predicts the driving mode of the user based on the genetic information of another user who is a user different from the user, the relationship between the driving mode of the other user and the genetic information of the user.

  Thereby, since the driving control device 100 according to the embodiment can predict with high accuracy what kind of driving tendency the user who cannot acquire the driving mode is, the driving predicted for the user who cannot acquire the driving mode. Control based on the tendency can be performed.

  Moreover, the driving | operation control part 134 performs the said predetermined control based on the said parameter | index based on the degree of a spatial recognition capability, a spatial intelligence, or a sleep depth as a constitution predicted from a user's genetic information.

  Thereby, if the driving control device 100 according to the embodiment has a tendency for a user who has a low spatial recognition capability, a low spatial intelligence, a shallow sleep depth, etc., to actually drive an accident, for example. Since a safety guide or the like can be given to this user, accidents can be effectively prevented.

  In addition, the driving control unit 134 performs predetermined control when it is determined that the user may perform dangerous driving based on the index.

  As a result, the operation control apparatus 100 according to the embodiment can accurately determine whether or not the user is likely to perform dangerous driving that leads to an accident, and thus is safer for a user who is likely to perform dangerous driving. Operation control can be realized.

  The operation control unit 134 performs predetermined control when it is determined that the user's constitution is involved in the occurrence of the accident based on the user's constitution and the user's accident history.

  Thereby, since the driving control device 100 according to the embodiment can accurately identify a user having a constitution that is involved in the occurrence of an accident, by performing suggestions and driving control for such a user, Generation | occurrence | production can be prevented effectively.

  The operation control unit 134 performs predetermined control at a timing based on the user's driving situation and the user's driving mode.

  Thereby, the operation control apparatus 100 according to the embodiment can effectively prevent the occurrence of an accident.

  The operation control unit 134 performs predetermined control at a timing at which the user does not drive in the driving mode based on the user's driving situation and the user's driving mode.

  Thereby, since the operation control apparatus 100 concerning embodiment can control before a user performs the driving | operation which leads to an accident, generation | occurrence | production of an accident can be prevented effectively.

  Further, the operation control unit 134 performs predetermined control before the user operates in the driving mode.

  Thereby, since the operation control apparatus 100 concerning embodiment can control before a user performs the driving | operation which leads to an accident, generation | occurrence | production of an accident can be prevented effectively.

  Moreover, the driving | operation control part 134 performs the proposal based on a user's driving | operation aspect with respect to a user as predetermined | prescribed control.

  Thereby, the operation control apparatus 100 according to the embodiment can make the user recognize that the user is driving dangerously.

  Further, the operation control unit 134 proposes not to drive in the driving mode, or proposes a safe driving mode instead of the driving mode.

  Thereby, the operation control apparatus 100 according to the embodiment can effectively prevent the occurrence of an accident.

  Moreover, the driving | operation control part 134 performs route guidance based on a user's driving | operation aspect as predetermined | prescribed control.

  Thereby, since the driving | running control apparatus 100 concerning embodiment can propose the route which is easy to drive | work which does not become dangerous driving | running | working, generation | occurrence | production of an accident can be prevented effectively.

  Moreover, the operation control part 134 performs operation control with respect to the mobile body driven by a user based on a user's driving | operation aspect as predetermined control.

  Thereby, since the driving | running control apparatus 100 concerning embodiment can avoid the dangerous driving | running | working by a user, it can prevent an accident.

  Although the embodiments of the present application have been described in detail with reference to some drawings, these are merely examples, and various modifications, including the aspects described in the disclosure section of the invention, based on the knowledge of those skilled in the art, It is possible to implement the present invention in other forms with improvements.

  In addition, the “section (module, unit)” described above can be read as “means” or “circuit”. For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

DESCRIPTION OF SYMBOLS 1 Driving control system 10 Car 30 Inspection organization server 60 Management server 100 Driving control device 121 Constitution information storage part 122 Gene information storage part 123 Driving history storage part 124 User constitution storage part 125 Driving tendency storage part 131 Acquisition part 132 Identification part 133 Evaluation Part 134 operation control part 135 prediction part

Claims (14)

An acquisition unit for acquiring genetic information of the user;
As an index indicating safety when driving by the user, as a constitution of the user predicted from the genetic information of the user , spatial recognition ability, spatial intelligence, or the degree of sleep depth And a control unit that performs predetermined control related to the user's operation. The acquisition unit further acquires the driving mode of the user,
The said control part performs the said predetermined control based on the said parameter | index based on the said user's constitution and the said user's driving | operation aspect estimated from the said user's genetic information. Operation control device. The apparatus further includes a prediction unit that predicts the driving mode of the user based on the relationship between the genetic information of another user who is a different user from the user and the driving mode of the other user and the genetic information of the user. And
The control unit performs the predetermined control based on the index based on the constitution of the user predicted from the genetic information of the user and the driving mode predicted by the prediction unit for the user. The operation control device according to claim 1. The said control part performs the said predetermined control, when it determines with the said user having a possibility of a dangerous driving based on the said parameter | index. The said any one of Claims 1-3 characterized by the above-mentioned. Operation control device. The control unit performs the predetermined control when it is determined that the user's constitution is involved in the occurrence of the accident based on the user's constitution and the user's accident history. The operation control device according to any one of claims 1 to 4 . The said control part performs the said predetermined control at the timing based on the said user's driving condition and the said user's driving | operation aspect. The driving | operation control apparatus as described in any one of Claims 1-5 characterized by the above-mentioned. . The control unit performs the predetermined control at a timing at which the user does not drive in the driving mode based on the driving status of the user and the driving mode of the user. The operation control apparatus according to any one of 1 to 6 . The operation control device according to claim 6 , wherein the control unit performs the predetermined control before the user operates in the operation mode. The operation control device according to any one of claims 1 to 8 , wherein the control unit makes a proposal based on the operation mode of the user to the user as the predetermined control. The operation control device according to claim 8 , wherein the control unit proposes not to operate in the operation mode, or proposes a safe driving mode instead of the driving mode. The operation control device according to any one of claims 8 to 10 , wherein the control unit performs route guidance as the predetermined control based on an operation mode of the user. Wherein, as the predetermined control, based on the operating mode of the user, any one of claims 1 to 11, characterized in that controlling the operation with respect to the moving body it is operated to the user The operation control device described in 1. An operation control method executed by the operation control device,
An acquisition step of acquiring genetic information of the user;
As an index indicating safety when driving by the user, as a constitution of the user predicted from the genetic information of the user , spatial recognition ability, spatial intelligence, or the degree of sleep depth And a control process for performing predetermined control related to the operation of the user based on the operation control method. An acquisition procedure for acquiring the genetic information of the user;
As an index indicating safety when driving by the user, as a constitution of the user predicted from the genetic information of the user , spatial recognition ability, spatial intelligence, or the degree of sleep depth An operation control program that causes a computer to execute a control procedure for performing predetermined control related to the operation of the user.

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