Patent Publication Number: US-2019188599-A1

Title: Information processing method, information processing apparatus, and program

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of priority of U.S. Provisional Patent Application No. 62/598745 filed on Dec. 14, 2017, and the benefit of priority of Japanese Patent Application Number 2018-139709 filed on Jul. 25, 2018, the entire contents of which is hereby incorporated by reference. 
    
    
     BACKGROUND 
     1. Technical Field 
     The present disclosure relates to information processing methods, information processing apparatuses, and programs for selecting sensor data as learning data. 
     2. Description of the Related Art 
     Patent Literature (PTL) 1 (Japanese Unexamined Patent Application Publication No. 2016-173682) discloses an error determination device that causes a selector or a learning agent to select information for learning from among image data each having an obtained detection value greater than or equal to a predetermined threshold value. 
     SUMMARY 
     However, in PTL 1, since detection values of a specific error determination device are used, data effective for learning in the specific error determination device are selected. Accordingly, the technique of PTL 1 cannot always ensure a diversity of learning data for configurations or techniques of unspecified devices. 
     In view of this, the present disclosure has an object to provide an information processing method, an information processing apparatus, and a program that can increase a diversity of learning data for configurations or techniques of unspecified devices. 
     An information processing method according to one aspect of the present disclosure includes the following executed by a computer: obtaining sensor data obtained by a sensor installed in a vehicle, and at least one type of traveling data of the vehicle; associating the sensor data and the at least one type of traveling data with each other; determining a degree of difference of the at least one type of traveling data from the at least one type of one or more traveling data associated with one or more sensor data; and selecting the sensor data as learning data according to the degree of difference. 
     Moreover, an information processing method according to another aspect of the present disclosure includes the following executed by a compute: obtaining sensor data obtained by a sensor installed in a vehicle, and at least one type of traveling data of the vehicle; associating the sensor data and the at least one type of traveling data with each other; making a computation model for determining a degree of difference of the at least one type of traveling data from the at least one type of one or more traveling data associated with one or more sensor data; and providing the computation model. 
     It should be noted that these general or specific aspects may be implemented by a system, a device, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, and may be implemented by any combination of a system, a device, an integrated circuit, a computer program, and a recording medium. 
     An information processing method, an information processing apparatus, and a program according to the present disclosure can increase a diversity of learning data for configurations or techniques of unspecified devices. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       These and other objects, advantages and features of the disclosure will become apparent from the following description thereof taken in conjunction with the accompanying drawings that illustrate a specific embodiment of the present disclosure. 
         FIG. 1  is a schematic view of an information processing system according to Embodiment  1 . 
         FIG. 2  is a block diagram illustrating an example of a hardware configuration of an information processing apparatus according to Embodiment  1 . 
         FIG. 3  is a block diagram illustrating an example of a hardware configuration of a vehicle according to Embodiment  1 . 
         FIG. 4  is a diagram illustrating an example of CAN (Controller Area Network) data. 
         FIG. 5  is a block diagram illustrating an example of a functional configuration of the information processing system according to Embodiment  1 . 
         FIG. 6  is a diagram for illustrating an association between image data and traveling data by an association unit of the information processing apparatus. 
         FIG. 7  is a table illustrating an example of combinations of associated image data and traveling data. 
         FIG. 8  is a conceptual diagram unidimensionally representing a normal distribution of types of traveling data. 
         FIG. 9  is a sequence diagram illustrating an example of operation in the information processing system. 
         FIG. 10  is a block diagram illustrating an example of a functional configuration of a vehicle according to Embodiment  2 . 
         FIG. 11  is a flow chart illustrating an example of operation of the vehicle according to Embodiment  2 . 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     Underlying Knowledge Forming the Basis of the Present Disclosure 
     In recent years, in the fields of automatic driving, security camera, robot, etc., objection detection using machine learning, such as deep learning for images captured by cameras, has been put into practical use. Such objection detection requires a large volume of teaching data for use in machine learning. For this purpose, myriad images captured by various cameras are collected, and teaching data are generated by humans giving correct interpretations to the collected images. 
     However, since giving correct interpretations to images by humans is costly, it is undesirable to generate teaching data simply from all obtained myriad images. Besides, even if teaching data are generated by giving correct interpretations to all myriad images without considering costs, machine learning need be executed for the obtained large volume of teaching data. As a result, it takes more processing load and processing time to execute machine learning. Accordingly, for efficient execution of machine learning, it is necessary to select images effective for machine learning from among myriad images. 
     Here, the myriad images used for machine learning need be made up of images captured in different situations, that is, diverse images. To put it differently, using images captured in different situations is more effective for achieving efficient machine learning than using images captured in similar situations. 
     As described above, the error determination device narrows down obtained image data to image data each having a detection value based on the image data greater than a predetermined threshold value, and causes a selector or a learning agent to select information for learning from among the narrowed-down image data. In other words, the error determination device uses detection results as selection indices for narrowing down image data to image data for machine learning. As a result, this technique is considered suitable for narrowing down image data to image data effective for machine learning in a specific error determination device. 
     However, since detection values to be obtained change as configurations or techniques of other devices change, the image data narrowed down to by the error determination device of PTL 1 do not always allow the configurations or techniques of the other devices to achieve efficient machine learning. In other words, with the conventional techniques, it is difficult to select sensor data for execution of efficient machine learning regardless of configurations or techniques of devices, from among a large volume of obtained sensor data. 
     In order to solve such a problem, an information processing method according to one aspect of the present disclosure includes the following executed by a computer: obtaining sensor data obtained by a sensor installed in a vehicle, and at least one type of traveling data of the vehicle; associating the sensor data and the at least one type of traveling data with each other; determining a degree of difference of the at least one type of traveling data from the at least one type of one or more traveling data associated with one or more sensor data; and selecting the sensor data as learning data according to the degree of difference. 
     With this, the degree of difference of the at least one type of traveling data from the at least one type of one or more traveling data associated with sensor data already selected as learning data for use in machine learning is determined, and the sensor data associated with the at least one type of traveling data having the determined degree of difference that is greater than or equal to a predetermined threshold value is selected as learning data. For this reason, it is possible to select the sensor data associated with the at least one type of traveling data dissimilar to the at least one type of one or more traveling data associated with the already selected sensor data, and select the sensor data obtained by sensing in a different situation. Accordingly, the above information processing method can increase a diversity of learning data for configurations or techniques of unspecified devices. In other words, it is possible to select sensor data for execution of efficient machine learning regardless of the configurations or techniques of the devices, from among a large volume of obtained sensor data. 
     Moreover, the information processing method may further include adding, to the one or more traveling data, the at least one type of traveling data associated with the sensor data selected as the learning data. 
     With this, the at least one type of one or more traveling data is updated, and thus it is possible to appropriately determine a degree of difference of traveling data associated with new sensor data in the determining. 
     Moreover, in the associating, the sensor data and the at least one type of traveling data obtained when the sensor data is sensed may be associated with each other. 
     With this, the sensor data is selected as the learning data using the at least one type of traveling data obtained when the sensor data is sensed, and thus it is possible to more accurately select the sensor data for the execution of efficient machine learning. 
     Moreover, the at least one type of traveling data may include at least two types of traveling data, and in the selecting, the sensor data may be selected as the learning data according to the degree of difference determined using the at least two types of traveling data. 
     With this, the sensor data for executing machine learning is selected using the at least two types of traveling, and thus it is possible to more accurately select the sensor data for the execution of the efficient machine learning. 
     Moreover, in the determining, the degree of difference may be determined using a combination of the at least two types of traveling data, and in the selecting, the sensor data may be selected as the learning data according to the degree of difference determined using the combination. 
     Moreover, in the determining, the degree of difference may be determined for each of the at least two types of traveling data, using the at least two types of traveling data, and in the selecting, the sensor data may be selected as the learning data according to the at least two degrees of difference determined respectively for the at least two types of traveling data. 
     Moreover, in the selecting, the at least two degrees of difference may be integrated, and the sensor data is selected as the learning data according to a result of the integration. 
     Moreover, in the determining, the degree of difference may be determined according to weight given to each of the at least two types of traveling data, using the at least two types of traveling data. 
     Moreover, in the selecting, the sensor data associated with the at least one type of traveling data may be selected as the learning data, the at least one type of traveling data having the degree of difference that is greater than or equal to a predetermined threshold value. 
     Moreover, the at least one type of traveling data may include any one of a location, time, weather, temperature, a traveling state, traveling speed, traveling control, and a driver attribute. 
     Moreover, the sensor may be an optical sensor. 
     Moreover, the information processing method may further include normalizing the at least one type of traveling data, and in the determining, the degree of difference may be determined using the at least one type of traveling data normalized. 
     Moreover, an information processing method according to another aspect of the present disclosure includes the following executed by a compute: obtaining sensor data obtained by a sensor installed in a vehicle, and at least one type of traveling data of the vehicle; associating the sensor data and the at least one type of traveling data with each other; making a computation model for determining a degree of difference of the at least one type of traveling data from the at least one type of one or more traveling data associated with one or more sensor data; and providing the computation model. 
     It should be noted that these general or specific aspects may be implemented by a system, a device, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, and may be implemented by any combination of a system, a device, an integrated circuit, a computer program, and a recording medium. 
     Hereinafter, an information processing method, an information processing apparatus, and a program according to one aspect of the present disclosure will be described in detail with reference to the drawings. 
     It should be noted that each of embodiments to be described below shows a specific example of the present disclosure. The numerical values, shapes, materials, structural components, the arrangement and connection of the structural components, steps, the processing order of the steps, etc. shown in the following embodiments are mere examples, and are not intended to limit the scope of the present disclosure. Furthermore, among the structural components in the following embodiments, components not recited in any of the independent claims which indicate the broadest concepts are described as optional structural components. 
     Embodiment 1 
     Hereinafter, Embodiment 1 will be described with reference to  FIG. 1  to  FIG. 9 . 
     1-1. Configuration FIG.  1  is a schematic view of an information processing system according to Embodiment  1 . 
     Specifically,  FIG. 1  shows information processing apparatus  100 , vehicle  200 , communication network  300 , and base station  310  for mobile communication system. For example, information processing system  1  includes, among these structural components, information processing apparatus  100  and vehicle  200 . It should be noted that  FIG. 1  shows one vehicle  200 , but there may be two or more vehicles  200  or at least one vehicle  100 . 
     Information processing apparatus  100  obtains images captured by camera  205  of vehicle  200 , and selects learning data for machine learning from among the obtained images. Information processing apparatus  100  is, for example, a server. 
     Vehicle  200  includes camera  205  and drives itself or assists with driving according to the results of object detection obtained using images captured by camera  205 . 
     Communication network  300  may be a general-purpose network, such as the Internet, or a special-purpose network. Base station  310  is, for example, a base station used in a mobile communication system such as the third generation mobile communication system (3G), the fourth generation mobile communication system (4G), or Long Term Evolution (LTE) (registered trademark). 
     Next, the following describes a specific example of a hardware configuration of information processing apparatus  100  with reference to  FIG. 2 . 
       FIG. 2  is a block diagram illustrating an example of a hardware configuration of an information processing apparatus according to Embodiment  1 . 
     As shown in  FIG. 2 , information processing apparatus  100  includes, as hardware components, processor  101 , main memory  102 , storage  103 , and communication interface (IF)  104 . 
     Processor  101  executes a control program stored in, for example, storage  103 . 
     Main memory  102  is a volatile storage area used as a work area when processor  101  executes a control program. 
     Storage  103  is a nonvolatile storage area that holds a control program or various data such as image data and traveling data. 
     Communication IF  104  communicates with vehicle  200  via communication network  300 . Communication IF  104  is, for example, a wired Local Area Network (LAN) interface. It should be noted that communication IF  104  may be a wireless LAN interface. Besides, communication IF  104  is not limited to a LAN interface, and may be any communication interface as long as the communication interface can establish communication connection with a communication network. 
     Next, the following describes a specific example of a hardware configuration of vehicle  200  with reference to  FIG. 3 .  FIG. 3  is a block diagram illustrating an example of a hardware configuration of an vehicle according to Embodiment  1 . 
     As shown in  FIG. 3 , vehicle  200  includes, as hardware components, processor  201 , main memory  202 , storage  203 , communication IF  204 , camera  205 , inertial measurement unit (IMU)  206 , global navigation satellite system (GNSS)  207 , and Controller Area Network (CAN) device  208 . 
     Processor  201  executes a control program stored in, for example, storage  203 . Processor  201  may include a processor used in an Electronic Control Unit (ECU) for vehicle  200  to drive itself or assist with driving. 
     Main memory  202  is a volatile storage area used as a work area when processor  201  executes a control program. 
     Storage  203  is a nonvolatile storage area that holds a control program or various data, such as image data and traveling data. 
     Communication IF  204  communicates with information processing apparatus  100  via communication network  300 . In other words, communication IF  204  may be any communication interface that can establish communication connection with communication network  300 . Specifically, communication IF  204  establishes communication connection with communication network  300  by establishing communication connection with base station  310  for mobile communication system. Communication IF  204  may be, for example, a wireless communication interface compliant with the communications standards used in a mobile communication system, such as the third generation mobile communication system (3G), the fourth generation mobile communication system (4G), or LTE (registered trademark). In addition, communication IF  204  may be a wireless local area network (LAN) interface compliant with, for example, IEEE 802.11a, 802.11b, 802.11g, 802.11n, and 802.11ac standards, and may be a communication interface that establishes communication connection with communication network  300  by establishing communication connection with a router not shown (e.g., a mobile wireless LAN router). 
     Camera  205  is an optical sensor including an optical system, such as a lens, and an image sensor, and is an example of a sensor installed in vehicle  200 . 
     IMU  206  is a sensor device including an acceleration sensor and a gyroscope sensor. The acceleration sensor measures acceleration of vehicle  200  in each of three different directions. The gyroscope sensor measures angular velocity of vehicle  200  at which a rotation is made about each of three axes that are the three different directions. 
     GNSS  207  receives information indicating a position of GNSS  207  from an artificial satellite, such as a global positioning system (GPS) satellite. In other words, GNSS  207  detects a current location of vehicle  200 . 
     CAN device  208  includes vehicle speed meter  208   a  that measures the traveling speed of vehicle  200 , and electronic control units (ECUs)  208   b  that control or monitor various devices included in vehicle  200 . CAN device  208  may include the above various devices. The various devices controlled by ECUs  208   b  are, for example, an engine, steering, a brake, an accelerator, and a shift lever, and measure CAN data. 
       FIG. 4  is a diagram illustrating an example of CAN data. 
     CAN data indicates traveling states of vehicle  200 , such as a traveling speed, an engine torque, a steering angle of steering, a brake oil pressure, an accelerator position, and a shift position, or traveling control, such as a steering control amount, an engine torque control amount, an engine speed control amount, a brake oil pressure control amount, and an accelerator position control amount. 
     Next, the following describes a functional configuration of information processing system  1  with reference to  FIG. 5 . 
       FIG. 5  is a block diagram illustrating an example of a functional configuration of the information processing system according to Embodiment  1 . It should be noted that communication network  300  and base station  310  in  FIG. 1  are omitted from  FIG. 5   
     First, the following describes a functional configuration of vehicle  200 . 
     Vehicle  200  includes, as functional components, detector  210 , memory  220 , and transmission unit  230   
     Detector  210  detects image data as sensor data of vehicle  200 , and traveling data of vehicle  200 . 
     Detector  210  detects image data at different timings. For example, the image data indicate moving images or still images obtained through capture by camera  205 . 
     Detector  210  detects traveling data at different timings. The traveling data comprise at least one type of data, and include, for example, one of a location, a traveling speed, traveling states, and traveling control. The location in the traveling data indicates a location of vehicle  200  detected by GNSS  207 . The traveling speed in the traveling data indicates a traveling speed of vehicle  200  detected by vehicle speed meter  208   a.  It should be noted that a traveling speed may be calculated with a detection value of IMU  206 , and may be calculated with a detection value of vehicle speed meter  208   a  and a detection value of IMU  206 . The traveling states in the traveling data indicate traveling states of vehicle  200  detected by ECUs  208   b.  As stated above, examples of the traveling states include a traveling speed, an engine torque, a steering angle of steering, a brake oil pressure, an accelerator position, and a shift position. The traveling control in the traveling data may indicate a control amount that is based on control of vehicle  200  by a driver and that is for controlling each device related to traveling of vehicle  200 , and may indicate an amount of control in processor  201  for controlling related to traveling of vehicle  200  enabled by automatic driving or drive assist. As stated above, the traveling control indicates, for example, a steering angle control amount, an engine torque control amount, an engine speed control amount, a brake oil pressure control amount, and an accelerator position control amount. These control amounts are detected from, for example, respective ECUs  208   b.    
     Regarding the image data, detector  210  stores into memory  220  frames included in the image data or still images and capturing timings that are times when the frames or still images are captured in association with each other. 
     Besides, regarding the at least one type of traveling data, detector  210  stores into memory  220  traveling data detected for respective types and detection timings that are times when the traveling data are detected in association with each other. 
     It should be noted that detector  210  may detect, as traveling data, time, weather, temperature such as outdoor temperature, outdoor humidity, a driver attribute of a driver in vehicle  200 , etc. 
     The time in the traveling data may be a time when traveling data is detected, and may indicate a time period that includes the time. Time periods may be classified as, for example, dawn, daytime, and nighttime, and the classifications may change according to periods of year such as summertime, wintertime, and an interim period. 
     The weather in the traveling data indicates weather at a current location of vehicle  200 . Detector  210  may detect weather by analyzing images captured by camera  205 , and may detect weather by retrieving weather of an area including a location of vehicle  200  at a time from an external server including weather information, according to the time and location. 
     The driver attribute in the traveling data is information indicating an attribute of the driver driving vehicle  200 , and is, for example, information indicating the gender, age, etc. of the driver. Detector  210  may detect a driver attribute by causing the driver to register the driver attribute with vehicle  200  in advance or by communicating with a terminal, such as a smartphone of the driver. It should be noted that once detector  210  detects a driver attribute, detector  210  need not repeatedly detect a driver attribute at timings until detector  210  detects a new driver attribute. 
     Detector  210  is implemented by, for example, camera  205 , IMU  206 , GNSS  207 , vehicle speed meter  208   a,  and ECUs  208   b.    
     Memory  220  stores image data detected by detector  210  together with capturing timings of the respective image data. In addition, memory  220  stores traveling data detected by detector  210  together with detection timings of the respective traveling data. Memory  220  is implemented by, for example, storage  203 . 
     Transmission unit  230  transmits image data and traveling data stored in memory  220  to information processing apparatus  100 . Transmission unit  230  may transmit image data and traveling data detected by detector  210  to information processing apparatus  100  on a regular basis, such as every day and every week. Moreover, for example, when vehicle  200  is in a parking space of vehicle  200 , such as the house of the driver, transmission unit  230  may transmit, to information processing apparatus  100 , image data and traveling data obtained when vehicle  200  was traveling until vehicle  200  was parked in the parking space. Furthermore, at this moment, when there are still image data and traveling data that have not been transmitted to information processing apparatus  100 , transmission unit  230  may transmit these image data and travelling data to information processing apparatus  100 . 
     Transmission unit  230  is implemented by, for example, processor  201 , main memory  202 , storage  203 , and communication IF  204 . 
     Next, the following describes a functional configuration of information processing apparatus  100 . 
     Information processing apparatus  100  includes, as functional components, obtainment unit  110 , association unit  120 , determination unit  130 , and selection unit  140 . Information processing apparatus  100  may further include learning database (DB)  150 . 
     Obtainment unit  110  obtains image data as sensor data obtained by camera  205  installed in vehicle  200 , and at least one type of traveling data of vehicle  200 . Obtainment unit  110  obtains image data and at least one type of traveling data from vehicle  200 . Obtainment unit  110  is implemented by, for example, processor  101 , main memory  102 , storage  103 , and communication IF  104 . 
     Association unit  120  associates the image data and at least one type of traveling data obtained by obtainment unit  110 . Association unit  120  associates image data with at least one type of traveling data at a time of capturing the image data. 
       FIG. 6  is a diagram for illustrating an association between image data and traveling data by an association unit of the information processing apparatus. In a in  FIG. 6 , shown is an example of detection timings for traveling data including location information and CAN data, and capturing timings for image data. In b in  FIG. 6 , shown is an example of combinations (sets) each comprising a different one of the image data and a different one of the traveling data associated with the different one image data which are detected at the respective timings shown in a in  FIG. 6 , in a relationship between the image data and the traveling data. 
     As shown in a in  FIG. 6 , location information P 1  to P 7 , CAN data D 1  to D 4 , and image data I 1  to  14  are detected at different first timings, different second timings, and different third timings, respectively. GNSS  207  that detects location information P 1  to P 7 , CAN device  208  that detects CAN data D 1  to D 4 , and camera  205  that captures image data I 1  to  14  do not generally perform detection or capturing in synchronization with each other, and thus the first timings, the second timings, and the third timings are likely to be different timings. In the example of a in  FIG. 6 , location information P 1  to P 5  are detected at respective timings t 1 , t 3 , t 5 , t 8 , and t 10 , CAN data Dl to D 3  are detected at respective timings t 2 , t 6 , and t 9 , and image data I 1  to I 3  are detected at respective timings t 4 , t 7 , and t 11 . Timings t 1  to t 11  are all different timings. As stated above, to be exact, there is a time lag between a timing at which image data is captured and a timing at which traveling data is detected. 
     Accordingly, for example, association unit  120  associates the latest traveling data when image data is captured, with the image data. In other words, association unit  120  associates traveling data detected at the latest timing among traveling data that have been detected until image data is captured, with the image data. 
     Specifically, association unit  120  associates, with image data I 1 , location information P 2  detected at latest timing t 3  among location information that have been detected until timing t 4  at which image data I 1  is captured, and CAN data D 1  detected at latest timing t 2  among CAN data that have been detected until timing t 4 . In consequence, as shown in b in  FIG. 6 , combination C 1  is obtained in which location information P 2 , CAN data D 1 , and image data I 1  are associated with each other. 
     Similarly, association unit  120  associates, with image data I 2 , location information P 3  detected at latest timing t 5  among location information that have been detected until timing t 7  at which image data I 2  is captured, and CAN data D 2  detected at latest timing t 6  among CAN data that have been detected until timing t 7 . In consequence, as shown in b in  FIG. 6 , combination C 2  is obtained in which location information P 3 , CAN data D 2 , and image data I 2  are associated with each other. 
     Similarly, association unit  120  associates, with image data I 3 , location information P 5  detected at latest timing t 10  among location information that have been detected until timing t 11  at which image data I 3  is captured, and CAN data D 3  detected at latest timing t 9  among CAN data that have been detected until timing t 11 . In consequence, as shown in b in  FIG. 6 , combination C 3  is obtained in which location information P 5 , CAN data D 3 , and image data I 3  are associated with each other. Association unit  120  may associate, as traveling data, timings t 4 , t 7 , and t 11  that are capturing timings for image data I 1  to I 3 , with respective combinations C 1  to C 3 . Accordingly, for example, as shown in  FIG. 7 , image data and traveling data are associated with each other. 
       FIG. 7  is a table illustrating an example of combinations of associated image data and traveling data. 
     It should be noted that although four image data I 1  to I 4  are shown as image data in a in  FIG. 6 , the present disclosure is not limited to this. At least five or at most three image data may be captured. Likewise, although seven location information P 1  to P 7  and four CAN data D 1  to D 4  are shown as traveling data, at most six or at least eight location information and at most three or at least five CAN data may be detected. 
     Moreover, although the example in which one data is obtained as CAN data is described in a in  FIG. 6 , various types of data included in CAN data are detected at different timings for the respective types. For this reason, when multiple types of data are included in CAN data, the multiple types of data may be associated with image data for the respective types in view of the timings at which the multiple types of data are detected. In this association, the latest data when image data is captured is associated with the image data for each of the multiple types of data in CAN data in the same manner as above. 
     Furthermore, although association unit  120  forms the above association for each of all image data I 1  to I 4 , the present disclosure is not limited to this. Association unit  120  may form the association for each image data representing N (where N is an integer of at least 2) images continuously captured among image data. In other words, association unit  120  may associate traveling data with image data captured at intervals of N-1 images among image data that are chronologically continuous. Accordingly, when a large volume of similar image data is obtained, such as a case in which image data comprise moving images and a time from when one image data is obtained to when the next image data is obtained is a short time like less than a few seconds, image data to be associated with may be narrowed down in advance. 
     Moreover, although association unit  120  associates the latest traveling data when image data is captured, with the image data, the present disclosure is not limited to this. For example, association unit  120  may associate traveling data detected at a timing closest to a timing at which image data is captured, with the image data. Besides, association unit  120  may associate traveling data first detected after a timing at which image data is captured, with the image data. Further, when GNSS  207 , CAN device  208 , and camera  205  are performing detection or capturing at a synchronized timing, association unit  120  may associate traveling data detected at a timing when camera  205  captures image data, with the image data. 
     Learning DB  150  includes other traveling data associated with other image data. The other traveling data are, for example, multiple types of traveling data as described above. Learning DB  150  may or may not include the other image data. The other image data are data already selected as learning data for use in machine learning. The other traveling data are respectively associated with the other image data and detected when the other image data are captured. Here, the other traveling data correspond to the above-described traveling data. It should be noted that an association between the other image data and the other traveling data may be formed through the same processing as in the association formed by association unit  120 . It should be noted that learning DB  150  may not include the other traveling data associated with the other image data, and may include another traveling data associated with one image data. Learning DB  150  is stored in, for example, storage  103 . 
     Determination unit  130  determines a degree of difference of multiple types of traveling data obtained by obtainment unit  110 , from the other traveling data in learning DB  150 , using combinations of the multiple types of traveling data. Determination unit  130  estimates, for example, a multivariate normal distribution that is a normal distribution in which a dimension is set for each type, using data values of multiple types of other traveling data included in learning DB  150 . Subsequently, determination unit  130  determines the degree of difference of the multiple types of traveling data obtained by obtainment unit  110 , from the other traveling data in learning DB  150 , by calculating a Mahalanobis distance of the multiple types of traveling data from the estimated multivariate normal distribution. Although determination unit  130  calculates the Mahalanobis distance as the degree of difference, the present disclosure is not limited to this. When a distribution of degrees, ranks, classifications, or traveling data having a correlation with the Mahalanobis distance is similar to a unique distribution, for example, sums of likelihoods for probability density functions of held traveling data may be determined as a degree of difference. In order to eliminate the influence of magnitude of a value for each type of traveling data, determination unit  130  may normalize the multiple types of traveling data through linear transformation as preprocessing so that the maximum value and the minimum value each become a constant value. Determination unit  130  is implemented by, for example, processor  101 , main memory  102 , and storage  103 . 
     Although determination unit  130  determines the degree of difference of the multiple types of traveling data obtained by obtainment unit  110 , from the other traveling data in learning DB  150 , using the combinations of the multiple types of traveling data, the present disclosure is not limited to this. For example, when obtainment unit  110  obtains one type of traveling data, determination unit  130  may determine a degree of difference of the obtained one type of traveling data from the other traveling data in learning DB  150 . In this case, determination unit  130  calculates, as the degree of difference, a difference from a standard value, such as a mean value and a median value of corresponding types of traveling data in learning DB  150 . 
     Selection unit  140  selects, as learning data, image data associated with multiple types of traveling data in a combination for which a degree of difference is determined by determination unit  130  using combinations of multiple types of traveling data, according to the degree of difference. For example, selection unit  140  determines whether a Mahalanobis distance calculated as a degree of difference by determination unit  130  is greater than or equal to a predetermined threshold value, and selects, as learning data, image data associated with traveling data of which the degree of difference is greater than or equal to the predetermined threshold value. 
       FIG. 8  is a conceptual diagram unidimensionally expressing a normal distribution of types of traveling data. 
     As shown in  FIG. 8 , according to a standard value calculated from a distribution curve unidimensionally expressing a normal distribution of multiple types of traveling data, selection unit  140  does not select, as learning data, image data associated with multiple types of traveling data having a degree of difference within a range of predetermined threshold value Th, and selects, as learning data, image data associated with multiple types of traveling data having a degree of difference outside of the range. For example, selection unit  140  does not select, as learning data, image data associated with multiple types of traveling data having degree of difference Dd 1  within a range of predetermined threshold value Th, and selects, as learning data, image data associated with multiple types of traveling data having degree of difference Dd 2  or Dd 3  outside of the range. 
     Selection unit  140  may add the multiple types of traveling data associated with the image data selected as the learning data, to the other traveling data in learning DB  150 . This updates learning DB  150 , and thus determination unit  130  can appropriately determine degree of differences of traveling data associated with new image data. 
     Selection unit  140  is implemented by, for example, processor  101 , main memory  102 , and storage  103 . 
     It should be noted that image data selected as learning data by information processing apparatus  100  is used as the learning data. For example, teaching data for use in machine learning is generated by giving a correct interpretation to the image data. 
     1-2. Operation 
     Next, the following describes operation of information processing system  1  according to Embodiment 1. 
       FIG. 9  is a sequence diagram illustrating an example of operation in the information processing system. 
     First, in vehicle  200 , detector  210  detects image data as sensor data and multiple types of traveling data at respective timings (S 11 ). The image data and multiple types of traveling data detected by detector  210  are stored into memory  220 . 
     Next, transmission unit  230  of vehicle  200  transmits the image data and multiple types of traveling data stored in memory  220  to information processing apparatus  100  via communication network  300  (S 12 ). 
     In information processing apparatus  100 , obtainment unit  110  obtains the image data and multiple types of traveling data transmitted from vehicle  200  (S 21 ). 
     Then, association unit  120  of information processing apparatus  100  associates the image data and the multiple types of traveling data (S 22 ). 
     Next, determination unit  130  of information processing apparatus  100  determines a degree of difference of the multiple types of traveling data (S 23 ). 
     Subsequently, selection unit  140  of information processing apparatus  100  selects, as learning data, the image data associated with the multiple types of traveling data of which the degree of difference is determined, according to the degree of difference determined by determination unit  130  (S 24 ). 
     It should be noted that the details of the processes in steps S 11 , S 12 , and S 21  to S 24  performed by the respective processing units are omitted because the details are already given when the functional configuration of vehicle  200  and the functional configuration of information processing apparatus  100  are described with reference to  FIG. 5 . 
     1-3. Advantageous Effects, etc. 
     The information processing method according to Embodiment 1 includes: obtaining image data obtained by camera  205  installed in vehicle  200 , and at least one type of traveling data of vehicle  200 ; associating the image data and the at least one type of traveling data with each other; determining a degree of difference of the at least one type of traveling data from the at least one type of one or more traveling data associated with one or more image data; and selecting the image data as learning data according to the degree of difference. 
     With this, the degree of difference of the at least one type of traveling data from the at least one type of one or more traveling data associated with image data already selected as learning data for use in machine learning is determined, and the image data associated with the at least one type of traveling data having the determined degree of difference that is greater than or equal to a predetermined threshold value is selected as learning data. For this reason, it is possible to select the image data associated with the at least one type of traveling data dissimilar to the at least one type of one or more traveling data associated with the already selected image data, and select the image data obtained by capturing in a different situation. Accordingly, the above information processing method can increase a diversity of learning data for configurations or techniques of unspecified devices. In other words, it is possible to select image data for execution of efficient machine learning regardless of the configurations or techniques of the devices, from among a large volume of obtained image data. 
     Moreover, with the information processing method according to Embodiment 1, the image data is selected as the learning data using the at least one type of traveling data obtained when the image data is captured, and thus it is possible to more accurately select the image data for the execution of the efficient machine learning. 
     Moreover, with the information processing method according to Embodiment 1, the image data for the execution of the machine learning is selected using the at least two types of traveling data, and thus it is possible to more accurately select the image data for the execution of the efficient machine learning. 
     Embodiment 2 
     Next, Embodiment 2 will be described with reference to  FIG. 10  and  FIG. 11 . 
     Information processing apparatus  100  according to Embodiment 1 is an external server of vehicle  200 . The present disclosure, however, is not limited to this, and information processing apparatus  100  may be installed in vehicle  200 . 
       FIG. 10  is a block diagram illustrating an example of a functional configuration of a vehicle according to Embodiment 2. 
     As shown in  FIG. 10 , vehicle  200 A includes detector  210  and information processing apparatus  100 A. Detector  210  is the same as the one in Embodiment 1, and thus description thereof is omitted. 
     Information processing apparatus  100 A differs from information processing apparatus  100  according to Embodiment 1 in including determination unit  130 A and memory  150 A. The other components are the same as those of information processing apparatus  100  according to Embodiment 1, and thus description thereof is omitted. 
     Memory  150 A stores information indicating a distribution based on other traveling data associated with image data already selected as learning data for use in machine learning. For example, the distribution is a multivariate normal distribution that is estimated using data values of multiple types of other traveling data and that is a normal distribution in which a dimension is set for each type. It should be noted that the information indicating the above distribution is an example of a computation model for determining a degree of difference. 
     Determination unit  130 A determines a degree of difference of multiple types of traveling data obtained by obtainment unit  110 , from the existing other traveling data, by calculating a Mahalanobis distance of the multiple types of traveling data from a multivariate normal distribution indicated in the information stored in memory  150 A without estimating the multivariate normal distribution. 
       FIG. 11  is a flow chart illustrating an example of operation of the vehicle according to Embodiment 2. 
     First, in vehicle  200 A, detector  210  detects image data as sensor data and multiple types of traveling data at respective timings (S 11 ). 
     By detector  210  detecting the image data and multiple types of traveling data in step S 11 , in information processing apparatus  100 A, obtainment unit  110  obtains the image data and multiple types of traveling data transmitted from vehicle  200 A (S 21 ). 
     Then, association unit  120  of information processing apparatus  100 A associates the image data and the multiple types of traveling data (S 22 ). 
     Next, determination unit  130 A of information processing apparatus  100 A determines a degree of difference of the multiple types of traveling data (S 23 A). 
     Subsequently, selection unit  140  of information processing apparatus  100 A selects, as learning data, the image data associated with the multiple types of traveling data of which the degree of difference is determined, according to the degree of difference determined by determination unit  130  (S 24 ). 
     It should be noted that the processes in respective steps S 11 , S 21 , S 22 , and S 24  are the same as those in Embodiment 1. Besides, the details of the process in step S 23 A performed by determination unit  130 A are omitted because the details are already given when the functional configuration of vehicle  200 A are described with reference to  FIG. 10 . 
     Information processing apparatus  100 A according to Embodiment 2 selects, as learning data, image data from among captured image data in vehicle  200 A, and can thus transmit, for example, only the selected image data to an information processing apparatus such as an external server. Accordingly, it is possible to reduce a communication volume from vehicle  200 A to an external server, which results in a reduction of communication load. 
     It should be noted that in information processing apparatus  100 A, memory  150 A stores the information indicating the distribution based on the other traveling data associated with the image data already selected as the learning data for use in machine learning, but the present disclosure is not limited to this. Information processing apparatus  100 A may be configured to obtain the information indicating the above distribution from another information processing apparatus, such as an external server. 
     In this case, the other information processing apparatus obtains sensor data obtained by a sensor installed in vehicle  200 A, and at least one type of traveling data of vehicle  200 A; associates the sensor data and the at least one type of traveling data; creates a computation model for determining a degree of difference of the at least one type of traveling data from the at least one type of one or more traveling data associated with one or more sensor data; and provides the created computation model. It should be noted that the obtainment and association of the sensor data and at least one type of traveling data by the other information processing apparatus are the same as those performed by obtainment unit  110  and association unit  120  of information processing apparatus  100  according to Embodiment 1. A computation model for determining a degree of difference may be an average value, a median value, etc. of traveling data for each type, in addition to a distribution based on other traveling data associated with image data already selected as learning data for use in machine learning. 
     3. Variations 
     3-1. Variation 1 
     In information processing apparatus  100  or  100 A according to Embodiment 1 or 2, determination unit  130  or  130 A determines a degree of difference using a combination of traveling data, but the present disclosure is not limited to this. A degree of difference may be determined for each type using traveling data. In this case, selection unit  140  selects, as learning data, image data according to at least two degrees of difference determined for respective types of traveling data. 
     Selection unit  140  may compare each of the at least two degrees of difference and a predetermined threshold value, and select, as learning data, image data associated with the traveling data when all of the at least two degrees of difference are greater than the corresponding predetermined threshold value. 
     Besides, selection unit  140  may integrate at least two degrees of difference and select image data as learning data according to the result of the integration. For example, selection unit  140  may integrate at least two degrees of difference by calculating a total sum, an average value, a median value, etc. of the at least two degrees of difference, and select, as learning data, image data associated with traveling data when the result of the integration is greater than a predetermined threshold value. 
     Determination unit  130 A may determine a degree of difference according to weight given to each of at least two types of traveling data, using the at least two types of traveling data. For example, determination unit  130 A may integrate degrees of difference obtained for respective types of traveling data, according to weight predetermined for each of the types of traveling data, and select, as learning data, image data associated with traveling data when the result of the integration is greater than a predetermined threshold value. For example, determination unit  130 A may determine a degree of difference according to weight determined in accordance with an index based on a distribution of each traveling data, such as a variance value obtained for each of types of traveling data. 
     3-2. Variation 2 
     In information processing apparatus  100  or  100 A according to Embodiment 1 or 2, association unit  120  associates image data and multiple types of traveling data, but image data may be associated with one type of traveling data. In this case, the one type of traveling data is one of a location, traveling speed, a traveling state, traveling control, a time, weather, temperature such as outdoor temperature, outdoor humidity, and a driver attribute of a driver in vehicle  200  or  200 A. 
     3-3. Variation 3 
     Information processing apparatus  100  or  100 A according to Embodiment 1 or 2 uses image data as an example of sensor data, but the present disclosure is not limited to this. Detection data obtained by light detection and ranging (LIDAR) may be used. In other words, vehicle  200  may include, instead of camera  205 , a LIDAR system that is a type of an optical sensor. Information processing apparatus  100  or  100 A may use, as sensor data, both image data and detection data obtained by LIDAR. Besides, detection data obtained by another optical sensor may be used as sensor data. 
     4. Others 
     It should be noted that in each of Embodiments 1 and 2, each of the structural components may be configured using dedicated hardware or may be realized by executing a software program suitable for each structural component. Each structural component may be realized by a program execution unit, such as a CPU or a processor, reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory. Here, the software for realizing the information processing method in each of Embodiments 1 and 2 is a program of the type described below. 
     Specifically, this program causes a computer to execute an information processing method, the information processing method including: obtaining sensor data obtained by a sensor installed in a vehicle, and at least one type of traveling data of the vehicle; associating the sensor data and the at least one type of traveling data with each other; making a computation model for determining a degree of difference of the at least one type of traveling data from the at least one type of one or more traveling data associated with one or more sensor data; and providing the computation model. 
     Although an information processing method, an information processing apparatus, and a program according to one or more aspects of the present disclosure have been described according to Embodiments 1 and 2, the present disclosure is not limited to Embodiments 1 and 2. 
     Although only some exemplary embodiments of the present disclosure have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of the present disclosure. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. 
     INDUSTRIAL APPLICABILITY 
     The present disclosure is useful as, for example, an information processing method, an information processing apparatus, and a program that can increase a diversity of learning data for configurations or techniques of unspecified devices.