Patent Publication Number: US-2022219680-A1

Title: Information processing device, information processing method, and non-transitory computer-readable medium storing information processing program

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority under 35 USC 119 from Japanese Patent Application No. 2021-004328 filed on Jan. 14, 2021, the disclosure of which is incorporated by reference herein. 
     BACKGROUND 
     Technical Field 
     The present disclosure relates to an information processing device, an information processing method, and a non-transitory computer-readable medium storing an information processing program, which detect the occurrence of impacts relating to collisions of a vehicle from the driving history of the vehicle, and the like. 
     Related Art 
     In market of used vehicles and the like, the value of a used vehicle differs due to the absence/presence of a history of accidents of that used vehicle. Even if a collision is minor (hereinafter called “light collision”), there are cases in which damage that requires repair is inflicted on the vehicle, and, in such cases, dealers and repair shops and the like require sensing of the fact that there was a collision. 
     When a vehicle collides, the airbags deploy, but, in actuality, airbags deploy only in the small fraction of collisions that are of the extent of damaging the frame of the vehicle body. Accordingly, light collisions must be detected accurately, regardless of the absence/presence of airbag operation. 
     Japanese Patent Application Laid-Open (JP-A) No. 2012-221407 discloses an evaluation index creating system for vehicles that estimates damage to vehicles by using stationary vibration load to a vehicle body as an index. 
     However, in the disclosure of JP-A No. 2012-221407, among the forces that are applied to the vehicle, acceleration components that are due to sudden acceleration, sudden braking and the like are not excluded from the acceleration components that are due to light collisions, and therefore, it is difficult to accurately detect light collisions. 
     SUMMARY 
     The present disclosure provides an information processing device, an information processing method, and a non-transitory computer-readable medium storing an information processing program, which may accurately detect light collisions. 
     A first aspect of the present disclosure is an information processing device including: a detecting section detecting an observed amount relating to a change in acceleration of a vehicle due to a driving operation; an inertial measurement section detecting an actually measured value of acceleration of the vehicle; and an estimating section that determines that the vehicle has collided in a case in which an acceleration difference, which is a difference between an actually measured value of acceleration of the vehicle detected by the inertial measurement section and an estimated value of acceleration of the vehicle derived on the basis of the observed amount, is greater than or equal to a predetermined threshold value. 
     The estimated value of the acceleration, which is derived on the basis of an observed amount relating to a change in the acceleration of the vehicle due to a driving operation, is the estimated value of the acceleration of the vehicle due to a driving operation. Further, the acceleration of the vehicle that arises at the time of a collision differs from a change in acceleration that is due to a driving operation. Accordingly, it can be determined that the vehicle has collided in a case in which the difference between the actually measured value of the acceleration of the vehicle that is detected by the inertial measurement section, and an estimated value of the acceleration of the vehicle that is derived on the basis of an observed amount relating to a change in the acceleration of the vehicle due to a driving operation, is greater than or equal to a predetermined threshold value. 
     In accordance with the information processing device of the first aspect, even in a light collision, divergence can arise between the actually measured value and the predicted value of the acceleration, and therefore, a collision of the vehicle may be inferred accurately. 
     In a second aspect of the present disclosure, in the above-described first aspect may be configured such that, in a case in which acceleration relating to the acceleration difference is acceleration that is inputted to the vehicle from a road surface through a tire, the estimating section does not employ the estimated value in determining a collision of the vehicle. 
     In accordance with the information processing device of the second aspect, a collision of the vehicle may be inferred accurately by not using input, which is from the road surface through a tire and which may be a determining error, as an actually measured value of the acceleration in the determining of a collision. 
     In a third aspect of the present disclosure, in the above-described second aspect, the detecting section may include a wheel speed detecting section that detects respective wheel speeds of four wheels that the vehicle has, and the estimating section may derive wheel speed differences that are respective differences between actually measured values of respective wheel speeds of the four wheels that are front and rear wheels of the vehicle detected by the wheel speed detecting section, and estimated values of respective wheel speeds of the four wheels derived on the basis of the observed amount, and, after a change in the wheel speed difference, in either of a case in which the acceleration difference changes or a case in which wheel speed differences of the front wheels and the rear wheels change in order, the estimating section may determine that acceleration relating to the acceleration difference is acceleration inputted from a road surface through a tire. Due to the above, components which may give rise to a determining error may be excluded from the acceleration. 
     In a fourth aspect of the present disclosure, in the above-described second aspect, the detecting section may include an imaging section that acquires image data of a periphery of the vehicle in time series, and the estimating section may consider acceleration, which relates to the acceleration difference at a time when sudden behavior of the vehicle is recorded in the image data acquired by the imaging section, to be acceleration that is inputted to the vehicle from a road surface through a tire. 
     In a fifth aspect of the present disclosure, in the above-described second aspect, the estimating section, in a case in which vertical direction displacement of position information of the vehicle derived on the basis of information from a satellite is greater than or equal to a predetermined value, may determine that acceleration, which relates to the acceleration difference, is acceleration that is inputted from a road surface through a tire. 
     In a sixth aspect of the present disclosure, in the above-described second aspect, the estimating section may refer to a database of places at which there are undulations of a road surface, places at which a slope changes sharply, and places at which changes in acceleration arise at plural vehicles, and may determine that acceleration relating to the acceleration difference is acceleration inputted from a road surface through a tire. 
     In a seventh aspect of the present disclosure, in the above-described aspects, the inertial measurement section may detect acceleration in a longitudinal direction and acceleration in a lateral direction of the vehicle respectively, and the estimating section may respectively derive an estimated value of acceleration in the longitudinal direction and an estimated value of acceleration in the lateral direction of the vehicle on the basis of the observed amount, and may determine that the vehicle has collided, in a case in which either of a difference between an actually measured value of acceleration in the longitudinal direction of the vehicle detected by the inertial measurement section and an estimated value of acceleration in the longitudinal direction of the vehicle, or a difference between an actually measured value of acceleration in the lateral direction of the vehicle detected by the inertial measurement section and an estimated value of acceleration in the lateral direction of the vehicle, is greater than or equal to a predetermined threshold value. 
     The information processing device of the seventh aspect may determine that there is a collision of the vehicle, in a case in which either of, a difference between an actually measured value and an estimated value of acceleration in the longitudinal direction of the vehicle, or a difference between an actually measured value and an estimated value of acceleration in the lateral direction of the vehicle, is greater than or equal to a predetermined threshold value. 
     In an eighth aspect of the present disclosure, in the above-described seventh aspect, the estimating section may estimate a damage direction of the vehicle on the basis of a quotient of a difference between the actually measured value of acceleration in the longitudinal direction of the vehicle detected by the inertial measurement section and the estimated value of acceleration in the longitudinal direction of the vehicle, and a difference between the actually measured value of acceleration in the lateral direction of the vehicle detected by the inertial measurement section and the estimated value of acceleration in the lateral direction of the vehicle. 
     The information processing device of the eighth aspect may estimate the damage direction of the vehicle on the basis of the acceleration in the longitudinal direction and the acceleration in the lateral direction of the vehicle. 
     In a ninth aspect of the present disclosure, in the above-described aspects, the estimating section may construct a model that estimates acceleration of the vehicle, on the basis of observed amounts relating to changes in acceleration of plural vehicles and actually measured values of acceleration of the plural vehicles, which are acquired in advance. 
     The information processing device of the ninth aspect constructs an acceleration estimating model by machine learning that is based on so-called big data that is acquired from a large number of vehicles. Therefore, a model that may accurately estimate the acceleration of the vehicle due to driving operation may be constructed. 
     In a tenth aspect of the present disclosure, in the above-described aspects, the observed amount may include measured values of vehicle motions of the vehicle, and control signals relating to driving operation amounts of the vehicle. 
     The information processing device of the tenth aspect may construct a model, which accurately estimates acceleration of a vehicle due to driving operation, by providing machine learning with observed amounts relating to changes in accelerations of vehicles due to driving operations. 
     An eleventh aspect of the present disclosure is an information processing method including: detecting, by a detecting section, an observed amount relating to a change in acceleration of a vehicle due to a driving operation; detecting, by an inertial measurement section, an actually measured value of acceleration of the vehicle; and determining that the vehicle has collided in a case in which an acceleration difference, which is a difference between the actually measured value of acceleration of the vehicle and an estimated value of acceleration of the vehicle derived on the basis of the observed amount, is greater than or equal to a predetermined threshold value. 
     The estimated value of the acceleration, which is derived on the basis of an observed amount relating to a change in the acceleration of the vehicle due to a driving operation, is the estimated value of the acceleration of the vehicle due to a driving operation. Further, the acceleration of the vehicle that arises at the time of a collision differs from a change in acceleration that is due to a driving operation. Accordingly, it may be determined that the vehicle has collided in a case in which the difference between the actually measured value of the acceleration of the vehicle that is detected by the inertial measurement section, and an estimated value of the acceleration of the vehicle that is derived on the basis of an observed amount relating to a change in the acceleration of the vehicle due to a driving operation, is greater than or equal to a predetermined threshold value. 
     In accordance with the information processing method of the eleventh aspect, even in a light collision, divergence may arise between the actually measured value and the predicted value of the acceleration, and therefore, a collision of the vehicle may be inferred accurately. 
     In a twelfth aspect of the present disclosure, in the above-described eleventh aspect may be configured such that, in a case in which acceleration relating to the acceleration difference is acceleration inputted to the vehicle from a road surface through a tire, the estimated value is not employed in determining a collision of the vehicle. 
     In accordance with the information processing method of the twelfth aspect, a collision of the vehicle may be inferred accurately by not using input, which is from the road surface through a tire and which may be a determining error, as an actually measured value of the acceleration in the determining of a collision. 
     In a thirteenth aspect of the present disclosure, in the above-described twelfth aspect, wheel speed differences, which are respective differences between actually measured values of respective wheel speeds of four wheels that are front and rear wheels of the vehicle, and estimated values of respective wheel speeds of the four wheels derived on the basis of the observed amount, are derived, and, after a change in the wheel speed difference, in either of a case in which the acceleration difference changes or a case in which wheel speed differences of the front wheels and the rear wheels change in order, it may be determined that acceleration relating to the acceleration difference is acceleration inputted from a road surface through a tire. Due to the above, components which may give rise to a determining error may be excluded from the acceleration. 
     A fourteenth aspect of the present disclosure is a non-transitory computer-readable medium storing an information processing program causing a computer to function as: an estimating section that determines that a vehicle has collided in a case in which an acceleration difference, which is a difference between an actually measured value of acceleration of the vehicle detected by an inertial measurement section and an estimated value of acceleration of the vehicle derived on the basis of an observed amount relating to a change in acceleration of the vehicle due to a driving operation detected by a detecting section, is greater than or equal to a predetermined threshold value. 
     The estimated value of the acceleration, which is derived on the basis of an observed amount relating to a change in the acceleration of the vehicle due to a driving operation, is the estimated value of the acceleration of the vehicle due to a driving operation. Further, the acceleration of the vehicle that arises at the time of a collision differs from a change in acceleration that is due to a driving operation. Accordingly, it may be determined that the vehicle has collided in a case in which the difference between the actually measured value of the acceleration of the vehicle that is detected by the inertial measurement section, and an estimated value of the acceleration of the vehicle that is derived on the basis of an observed amount relating to a change in the acceleration of the vehicle due to a driving operation, is greater than or equal to a predetermined threshold value. 
     In accordance with the non-transitory computer-readable medium storing the information processing program of the fourteenth aspect, even in a light collision, divergence can arise between the actually measured value and the predicted value of the acceleration, and therefore, a collision of the vehicle may be inferred accurately. 
     In a fifteenth aspect of the present disclosure, in the above-described fourteenth aspect may be configured such that, in a case in which acceleration relating to the acceleration difference is acceleration inputted to the vehicle from a road surface through a tire, the estimated value is not employed in determining a collision of the vehicle. 
     In accordance with the non-transitory computer-readable medium storing the information processing program of the fifteenth aspect, a collision of the vehicle can be inferred accurately by not using input, which is from the road surface through a tire and which may be a determining error, as an actually measured value of the acceleration in the determining of a collision. 
     In a sixteenth aspect of the present disclosure, in the above-described fifteenth aspect, wheel speed differences, which are respective differences between actually measured values of respective wheel speeds of four wheels that are front and rear wheels of the vehicle, and estimated values of respective wheel speeds of the four wheels derived on the basis of the observed amount, are derived, and, after a change in the wheel speed difference, in either of a case in which the acceleration difference changes or a case in which wheel speed differences of the front wheels and the rear wheels change in order, it may be determined that acceleration relating to the acceleration difference is acceleration inputted from a road surface through a tire. Due to the above, components which may give rise to a determining error may be excluded from the acceleration. 
     According to the above aspects, the information processing device, information processing method, and non-transitory computer-readable medium storing information processing program of the present disclosure may accurately detect light collisions. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Exemplary embodiments will be described in detail based on the following figures, wherein: 
         FIG. 1  is a schematic drawing illustrating the configuration of an information processing device relating to an exemplary embodiment; 
         FIG. 2  is a block drawing illustrating the configuration of a vehicle; 
         FIG. 3  is a block drawing illustrating an example of concrete configurations of a calculating server relating to the present exemplary embodiment; 
         FIG. 4  is a functional block drawing of a CPU of the calculating server; 
         FIG. 5  is a functional block drawing of the CPU of the calculating server after learning for acceleration estimation; 
         FIG. 6  is a functional block drawing of the CPU of the calculating server after learning for wheel speed estimation; 
         FIG. 7  is a functional block drawing of the CPU of the calculating server after learning for road surface input estimation; 
         FIG. 8  is a flowchart illustrating an example of processings at the calculating server relating to the present exemplary embodiment; 
         FIG. 9  is a schematic drawing of a case in which accident determining is carried out on the basis of elements that are an acceleration difference, an FL wheel speed difference, an RL wheel speed difference, pedal operation by the driver, latitude/longitude, and the like; 
         FIG. 10  is a schematic drawing at the time of carrying out accident determination on the basis of differences between actually measured values and estimated values estimated by an RNN; 
         FIG. 11  is a schematic drawing illustrating an example of values detected by various sensors; 
         FIG. 12A  is an example of an image in a case in which the vehicle starts to ride-over a step; 
         FIG. 12B  is an explanatory drawing illustrating a case in which, at the time when the vehicle rides-over a step, the left and right front wheels rotate idly and the speeds thereof increase, and thereafter, the left and right rear wheels rotate idly and the speeds thereof increase; 
         FIG. 13A  is an example of an image in a case in which the vehicle passes over a rough road surface; 
         FIG. 13B  is an explanatory drawing illustrating a case in which two maximum values of the acceleration difference are observed, and speeds of the front left wheel and the rear left wheel respectively increase immediately before the respective maximum values; 
         FIG. 14A  is an example of an image that is acquired by an imaging device of the vehicle and is of a case in which the front right wheel of the vehicle rotates idly, when the driver depresses the accelerator pedal at the time of making a right turn over the step between a roadway and a sidewalk; 
         FIG. 14B  is an explanatory drawing illustrating a case in which, as a result of the front right wheel of the vehicle rotating idly and the speed thereof increasing, divergence arises between actually measured values (the solid line) and estimated values (the dotted line) of the wheel speed at the front right wheel; 
         FIG. 15A  is an example of an image that is acquired by the imaging device of the vehicle and is of a case in which unloading (pitching) of the rear left wheel of the vehicle arises; 
         FIG. 15B  is an explanatory drawing illustrating a case in which, as a result of the rear left wheel of the vehicle rotating idly, divergence arises between actually measured values (the solid line) and estimated values (the dotted line) of the wheel speed at the rear left wheel; 
         FIG. 16  is a block drawing illustrating an example of employment of the information processing device relating to the present exemplary embodiment; and 
         FIG. 17  is a block drawing illustrating another example of employment of the information processing device relating to the present exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     An information processing device  100  relating to a present exemplary embodiment is described hereinafter by using  FIG. 1 . The information processing device  100  illustrated in  FIG. 1  includes: a communication device  110  that acquires data from plural vehicles  200  that are so-called connected cars that have the function of being continuously connected to a network; a data storage  120  that accumulates data received by the communication device  110 ; a computing server  10  that asks the data storage  120  for data needed for machine learning using a neural network, and that, by the machine learning that is carried out on the basis of the data acquired from the data storage  120 , constructs an acceleration estimating model for estimating acceleration of the vehicle  200 , and, on the basis of the constructed acceleration estimating model and the data acquired from the data storage  120 , predicts the acceleration of the vehicle  200 ; and a business terminal  130  that is configured so as to be able to communicate with the individual vehicles  200  via the communication device  110  on the basis of notifications from the computing server  10 . 
     As will be described later, the data storage  120  is a data server that has a database, and the computing server  10  is a computer that can execute advanced computing processings at high speed. The data storage  120  and the computing server  10  each may be a unit server, or may be a cloud that can disperse the processing load. The data storage  120  and the computing server  10  may be the same server. The business terminal  130  is not a requisite configuration, and may be omitted depending on the mode of the services relating to the vehicles  200 . 
       FIG. 2  is a block drawing illustrating the configuration of the vehicle  200 . The vehicle  200  is configured by: a storage device  18  that stores data needed for computing by a computing device  14  and the results of computing by the computing device  14 ; an image information processing section  20  that derives behaviors of the vehicle  200  from image information acquired by an imaging device  22 ; an input device  12  to which are inputted the information relating to the behaviors of the vehicle  200  that were derived by the image information processing section  20 , the vehicle longitudinal speed detected by a vehicle speed sensor  24 , the deviation and acceleration of the azimuth angle of the vehicle  200  detected by an IMU (inertial measurement unit)  26 , the steering angle of the vehicle  200  detected by a steering angle sensor  28 , the throttle opening degree of the vehicle  200  detected by a throttle sensor  30 , the force of depressing the brake pedal of the vehicle  200  detected by a brake pedal sensor  32 , and information acquired by a V2X communication section  34  by wireless communication; the computing device  14  that, on the basis of inputted data that was inputted from the input device  12  and data stored in the storage device  18 , estimates the acceleration of the vehicle  200  as needed; and an outputting device  16  that outputs the results of computing of the computing device  14  to the V2X communication section  34 . Further, a master cylinder sensor, which detects the pressure within the master cylinder of the brakes, may be provided separately at the vehicle  200  in addition to the brake pedal sensor  32 . 
     As mentioned above, the vehicle  200  is a so-called connected car, but, if not a connected car, may be a vehicle that is retrofitted with a communicator that is installed afterwards such as a so-called transaction log or the like that analyzes/utilizes traveling data transmitted from onboard equipment installed in the vehicle  200 , and various sensors that acquire traveling data. 
     The imaging device  22  relating to the present exemplary embodiment is an onboard camera or the like, and acquires image data of the periphery of the vehicle  200 . Further, the vehicle speed sensor  24  is configured so as to detect the respective four wheel speeds of the vehicle  200 . 
       FIG. 3  is a block drawing illustrating an example of the concrete configuration of the computing server  10  relating to the exemplary embodiment of the present disclosure. The computing server  10  is configured to include a computer  40 . The computer  40  has a CPU (Central Processing Unit)  42 , a ROM (Read Only Memory)  44 , a RAM (Random Access Memory)  46 , and an input/output port  48 . As an example, the computer  40  is preferably a type that can execute advanced computing processings at high speed. 
     At the computer  40 , the CPU  42 , the ROM  44 , the RAM  46  and the input/output port  48  are connected to one another via various buses such as an address bus, a data bus, a control bus and the like. A display  50 , a mouse  52 , a keyboard  54 , a hard disk (HDD)  56 , and a disk drive  60 , which can read-out information from various disks (e.g., a CD-ROM, a DVD, and the like)  58 , are respectively connected to the input/output port  48  as various input/output devices. 
     A network  62  is connected to the input/output port  48 , and information can be transmitted to and received from various devices that are connected to the network  62 . In the present exemplary embodiment, the data storage  120 , which is a data server to which a database (DB)  122  is connected, is connected to the network  62 , and information can be transmitted to and received from the DB  122 . 
     Time series data and the like of the plural vehicles  200 , which data is acquired via the communication device  110 , is stored in the DB  122 . The storing of data into the DB  122  can be carried out by, other than via the communication device  110 , registration therein by various devices that are connected to the computer  40  or the network  62 . 
     The present exemplary embodiment describes that time series data and the like of the plural vehicles  200  are stored in the DB  122  that is connected to the data storage  120 . However, the information of the DB  122  may be stored in the HDD  56  that is built into the computer  40 , or in an external storage such as an externally-attached hard disk or the like. 
     A program relating to machine learning using a neural network is installed in the HDD  56  of the computer  40 . In the present exemplary embodiment, due to the CPU  42  executing this program, machine learning starts, and a trained model that is based on machine learning is constructed. Moreover, the acceleration of the vehicle  200  is estimated by using the trained model that has been constructed. Further, the CPU  42  displays the results of processing by this program on the display  50 . 
     There are several methods for installing the program, which relates to machine learning of the present exemplary embodiment, in the computer  40 . For example, the program is stored together with a set-up program on a CD-ROM or a DVD or the like, and the disk is set in the disk drive  60 , and the program is installed in the HDD  56  by the CPU  42  executing the set-up program. Or, the program may be installed in the HDD  56  by communication with another information processing device that is connected to the computer  40  via a dial-up line or the network  62 . 
       FIG. 4  is a functional block drawing of the CPU  42  of the computing server  10 . The respective functions that are realized by the CPU  42  of the computing server  10  executing a program relating to machine learning are described. The program relating to machine learning has a pre-processing function that derives the accumulated value and the average value and the like of data that is transmitted-in from the vehicle  200 , a data selecting function that selects data that is to be provided to the machine learning, a model generating function that generates an acceleration estimating model, a wheel speed estimating model and a road surface input estimating model, a learning function that provides the selected data to a candidate model as teaching data, and executes machine learning, an evaluating function that evaluates the performance of the trained model by actually measured values (measured values) that are teaching data, and choosing function that chooses a trained model that has excellent performance. Due to the CPU  42  executing the program relating to machine learning that has these respective functions, the CPU  42  functions as a pre-processing section  72 , a data selecting section  74 , a model generating section  76 , a learning section  78 , an evaluating section  80  and a choosing section  82  as illustrated in  FIG. 4 . 
       FIG. 5  is a functional block drawing of the CPU  42  of the computing server  10  after learning for acceleration estimation. The CPU  42  after learning has a pre-processing function that derives the accumulated value and the average value and the like of data that is transmitted-in from the vehicle  200 , a data selecting function that selects data for collision sensing, an acceleration estimating function that estimates the acceleration of the vehicle  200  by using the trained model, a difference deriving function that derives the difference between an actually measured value and a presumed value, a determining function that determines the absence/presence of a collision, and a damage direction estimating function that estimates the damage direction due to the collision. Due to the CPU  42  executing the program relating to collision sensing that has these respective functions, the CPU  42  functions as a pre-processing section  84 , a data selecting section  86 , an acceleration estimating section  88 , a difference deriving section  90 , a determining section  92  and a damage direction estimating section  94  as illustrated in  FIG. 5 . 
       FIG. 6  is a functional block drawing of the CPU  42  of the computing server  10  after learning for wheel speed estimation. The CPU  42  after learning has a pre-processing function that derives the accumulated value and the average value and the like of data that is transmitted-in from the vehicle  200 , a data selecting function that selects data for wheel speed estimation, a wheel speed estimating function that estimates the respective wheel speeds of the vehicle  200  by using the trained model, a difference deriving function that derives the difference between an actually measured value and a presumed value, and a determining function that determines the absence/presence of a collision and of road surface input that is force inputted from the road surface through a tire to the vehicle  200 . Due to the CPU  42  executing the program relating to collision sensing that has these respective functions, the CPU  42  functions as a pre-processing section  184 , a data selecting section  186 , a wheel speed estimating section  188 , a difference deriving section  190 , and a determining section  192  as illustrated in  FIG. 6 . 
       FIG. 7  is a functional block drawing of the CPU  42  of the computing server  10  after learning for road surface input estimation. The CPU  42  after learning has a pre-processing function that derives the accumulated value and the average value and the like of data that is transmitted-in from the vehicle  200 , a data selecting function that selects data for road surface input estimation, a road surface input estimating function that estimates input from the road surface to the vehicle  200  through a tire by using the trained model, and a determining function that determines the absence/presence of road surface input. Due to the CPU  42  executing the program relating to collision sensing that has these respective functions, the CPU  42  functions as a pre-processing section  284 , a data selecting section  286 , a road surface input estimating section  288 , and a determining section  290  as illustrated in  FIG. 7 . 
       FIG. 8  is a flowchart illustrating an example of processings at the computing server  10  relating to the present exemplary embodiment. The left side of  FIG. 8  is a thread relating to the constructing of a model for acceleration estimation (hereinafter called “acceleration estimating model”), a model for wheel speed estimation (hereinafter called “wheel speed estimating model”), and a model for estimating the absence/presence of road surface input (hereinafter called “road surface input estimating model”), respectively by machine learning. The right side of  FIG. 8  is a thread relating to collision sensing by using the acceleration estimating model, the wheel speed estimating model and the road surface input estimating model after learning. 
     In step  600 , in order to construct the acceleration estimating model, collecting of various traveling data of the plural vehicles  200  from the data storage  120  is carried out. Because data, which is needed for the pre-processing that derives the accumulated value and the average value and the like, exists among the various traveling data of the vehicles  200 , this data is processed in step  600 . 
     In step  602 , data selection, which selects various traveling data that could become teaching data used in machine learning, is carried out. The various traveling data that could become teaching data include, for example: measured values of vehicle motion that mechanically relate to the longitudinal and lateral accelerations and the wheel speeds of the vehicle  200 , beginning with the vehicle speed; operation states of vehicle motion control that are causes of increasing/decreasing the longitudinal and lateral accelerations and the wheel speeds of the vehicle  200 , beginning with the ABS (Antilock Braking System); control signals that express driving operations by the driver that are causes of increasing/decreasing the longitudinal and lateral accelerations and the wheel speeds such as the pedals and the steering wheel and the like of the vehicle  200 ; control signals that express operations of power units that are causes of increasing/decreasing the longitudinal and lateral accelerations and the wheel speeds such as the engine and the like; and the like. In addition, measured values of the vehicle exterior environment that indirectly affect increasing/decreasing of the longitudinal and lateral accelerations and the wheel speeds such as the external air temperature and the wipers and the like, and control signals expressing operations of onboard equipment that are used depending on vehicle exterior environment, may be used. Data of the exterior that is generally disclosed by a meteorological agency or the like may be used as the measured values of the vehicle exterior environment, rather than values obtained by onboard sensors. In the data selection of step  602 , various traveling data of the vehicles  200  under a broad range of conditions such as geographical regions and weather conditions and the like, are collected as the teaching data. Further, a robust acceleration estimating model can be constructed by incorporating into the teaching data, as much as possible, marked changes in the acceleration due to sudden braking or the like of the vehicle  200 , extreme driving operations such as sudden turning of the steering wheel or the like, extreme motion states of the vehicle  200  such as high speeds or the like, control states that arise rarely such as ABS operation, and the like. 
     The teaching data may include acceleration data relating to accidents. Accidents are extremely rare, and, in principle, are difficult to predict from driving operations. Therefore, even if such data is included in the teaching data, the effects thereof the constructing of the model are minute. 
     In step  604 , a candidate model for acceleration estimation is generated, and machine learning is executed by providing the candidate model with the selected various traveling data as teaching data. The model that executes machine learning in the present exemplary embodiment is a model that estimates acceleration of the vehicle  200  due to driving operations, and does not estimate acceleration due to a collision. In other words, acceleration that cannot be estimated is considered to be force from the vehicle exterior, and cases in which such acceleration arises are determined to be the possibility of a collision. 
     In step  604 , individual models may be constructed per configuration of the vehicle  200 , such as per vehicle type, year model, grade, tires that are used and the like. Further, models may be constructed in accordance with the country, season and weather in which the vehicle  200  is used. Or, a candidate model for acceleration estimation may be constructed by adding, to the above-described various traveling data, conditions relating to configurations of the vehicle  200  such as the vehicle type, the year model, the grade, the tires that are used and the like, and the country, season and weather in which the vehicle  200  is used. 
     In the present exemplary embodiment, for example, LSTM (Long Short-Term Memory) is employed in the architecture of an artificial regression neural network (RNN) that handles time series data. LSTM is architecture that can internally carry out processings such as (1) estimating the acceleration of the vehicle  200  from driving operations at a same point in time and the history of the driving operations, (2) estimating the acceleration of the vehicle  200  from the vehicle motions up until immediately before, (3) estimating the acceleration of the vehicle  200  from the correspondence between driving operations and vehicle motions up until immediately before, and the like. 
     In LSTM, sensor value vectors x 1,t , x 2,t  are respectively defined as the vectors of the output values of the various sensors at time t (hereinafter called sensor values). The sensor value vector x 1,t  has, as the elements thereof, actually measured values of vehicle motions that mechanically relate to the longitudinal and lateral accelerations and the wheel speeds of the vehicle  200 , beginning with the vehicle speed. The sensor value vector x 2,t  has, as the elements thereof, control signals expressing driving operations by the driver that are causes of increasing/decreasing the longitudinal and lateral accelerations and the wheel speeds such as the pedals and the steering wheel and the like of the vehicle  200 , and control signals expressing operations of power units that are causes of increasing/decreasing the longitudinal and lateral accelerations and the wheel speeds such as the engine and the like, and the like. 
     Because the sensor value vectors x 1,t , x 2,t  are time series data, sensor value matrix x t , which is for estimating acceleration of the vehicle  200  at time t, is defined as follows. In the sensor value matrix x t , m is an arbitrary mask value. 
     
       
         
           
             
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     In the present exemplary embodiment, model f that estimates acceleration of the vehicle  200  is constructed by using LSTM from time series data that is stored in the sensor value matrix x t . As model f of the present exemplary embodiment, there are model f fb (x t ) that estimates the longitudinal acceleration of the vehicle  200 , and model f lr (x t ) that estimates the lateral acceleration of the vehicle  200 . 
     LSTM is an algorithm that predicts output from inputted time series data, and, through a process that updates past data to new data, constructs a model from which predicted values are outputted. By using, as teaching data, the sensor value matrix x t  that is based on the data selected in step  602  as time series data, following model f fb (x t ) that estimates the longitudinal acceleration of the vehicle  200  and following model f lr (x t ) that estimates the lateral acceleration of the vehicle  200  are constructed. In the present exemplary embodiment, as illustrated in  FIG. 1 , because a large amount of data relating to the sensor value matrix x t  is collected from a large number of the vehicles  200 , model construction by LSTM is easy. 
     The respective acceleration estimating models f fb (x t ), f lr (x t ) may be constructed as statistical models in accordance with a non-neural network, or may be constructed on the basis of equations of motion of the vehicle  200 . 
         â   fb,t   =f   fb ( x   t ) 
         â   lr,t   =f   lr ( x   t )     â fb,t : estimated value of longitudinal acceleration   â lr,t : estimated value of lateral acceleration   
     In step  606 , the respective performances of the constructed models f fb (x t ), f lr (x t ) are evaluated by new time series data that are actually measured values, and the models f fb (x t ), f lr (x t ) in which the errors between the actually measured values and the estimated values are in an allowable range are outputted, and processing is ended. The models f fb (x t ), f lr (x t ) that are outputted in step  606  are used in the collision sensing thread. 
     In step  610 , in order to construct the wheel speed estimating model, collecting of various traveling data of plural vehicles  200  from the data storage  120  is carried out. In a case in which data, which is needed for the pre-processing that derives the accumulated value and the average value and the like, exists among the various traveling data of the vehicles  200 , this data is processed in step  610 . 
     In step  612 , data selection, which selects various traveling data that could become teaching data to be used in machine learning, is carried out. The various traveling data that could become teaching data are the same various traveling data that are listed as examples in above-described step  602 . 
     In step  614 , a candidate model for wheel speed estimation is generated, and machine learning is executed by providing the candidate model with the selected various traveling data as teaching data. The model that executes machine learning in the present exemplary embodiment is a model that estimates the respective wheel speeds of the four wheels that are the front and rear wheels of the vehicle  200 . 
     In step  614 , in the same way as in step  604 , individual models may be constructed per configuration of the vehicle  200 , such as per vehicle type, year model, grade, tires that are used and the like. Further, models may be constructed in accordance with the country, season and weather in which the vehicle  200  is used. Or, a candidate model for wheel speed estimation may be constructed by adding, to the above-described various traveling data, conditions relating to configurations of the vehicle  200  such as the vehicle type, the year model, the grade, the tires that are used and the like, and the country, season and weather in which the vehicle  200  is used. In step  614 , in the same way as in step  604 , for example, LSTM is employed in the architecture of the RNN. 
     In step  614 , in the same way as in step  604 , sensor value vectors x 1,t , x 2,t  are respectively defined as the vectors of the sensor values of time t. The sensor value vector x 1,t  has, as the elements thereof, measured values of vehicle motions that mechanically relate to the longitudinal and lateral accelerations and the wheel speeds of the vehicle  200 , beginning with the vehicle speed. The sensor value vector x 2,t  has, as the elements thereof, control signals expressing driving operations by the driver that are causes of increasing/decreasing the longitudinal and lateral accelerations and the wheel speeds of the vehicle  200  such as the pedals and the steering wheel and the like, and control signals expressing operations of power units that are causes of increasing/decreasing the longitudinal and lateral accelerations and the wheel speeds such as the engine and the like, and the like. 
     In step  614 , in the same way as in step  604 , the sensor value matrix x t  is defined. Then, model f vx (x t ), which estimates the wheel speed of the vehicle  200  from the time series data that is stored in the sensor value matrix x t , is constructed by using LSTM. The wheel speed estimating model f vx (x t ) may be constructed as a statistical model in accordance with a non-neural network, or may be constructed on the basis of equations of motion of the vehicle  200 . 
     In step  616 , the performance of the constructed model f vx (x t ) is evaluated by new time series data that are actually measured values, and the model f vx (x t ) in which the errors between the actually measured values and the estimated values are in an allowable range is outputted, and processing is ended. The model f vx (x t ) that is outputted in step  616  is used in the collision sensing thread. 
     In step  620 , in order to construct the road surface input estimating model, collecting of various traveling data of the plural vehicles  200  from the data storage  120  is carried out. In a case in which data, which is needed for the pre-processing that derives the accumulated value and the average value and the like, exists among the various traveling data of the vehicles  200 , this data is processed in step  620 . 
     In step  622 , data selection, which selects various traveling data that could become teaching data used in machine learning, is carried out. The various traveling data that could become teaching data are the same various traveling data that are listed as examples in above-described step  602 . However, in most cases, undulations of a road surface that give rise to road surface input arise at the same places, and therefore, data of projection/indentation positions of road surfaces that are based on position measuring data of a GPS (Global Positioning System) are included. 
     In step  624 , a candidate model for road surface input estimation is generated, and machine learning is executed by providing the candidate model with the selected various traveling data as teaching data. The model that executes machine learning in the present exemplary embodiment is a model that estimates road surface input from information such as changes in the acceleration of the vehicle  200 , changes in the respective wheel speeds of the four wheels that are the front and rear wheels, positions at which of projections/indentations exist on road surfaces, and the like. 
     In step  624 , in the same way as in step  604 , individual models may be constructed per configuration of the vehicle  200 , such as per vehicle type, year model, grade, tires that are used and the like. Further, models may be constructed in accordance with the country, season and weather in which the vehicle  200  is used. Or, a candidate model for road surface input estimation may be constructed by adding, to the above-described various traveling data, conditions relating to configurations of the vehicle  200  such as the vehicle type, the year model, the grade, the tires that are used and the like, and the country, season and weather in which the vehicle  200  is used. In step  624 , in the same way as in step  604 , for example, LSTM is employed in the architecture of the RNN. 
     In step  624 , in the same way as in step  604 , a model that estimates road surface input of the vehicle  200  from time series data is constructed by using LSTM. The road surface input estimating model may be constructed as a statistical model in accordance with a non-neural network, or may be constructed on the basis of equations of motion of the vehicle  200 . 
     In step  626 , the performance of the constructed model is evaluated by new time series data that are actually measured values, and the model in which the errors between the actually measured values and the estimated values are in an allowable range is outputted, and processing is ended. The model that is outputted in step  626  is used in the collision sensing thread. 
       FIG. 9  is a schematic drawing in a case of carrying out accident determination on the basis of elements such as the difference between the measured value and the estimated value of acceleration (hereinafter called “acceleration difference”), the difference between the measured value and the estimated value of the FL (front left wheel) wheel speed (hereinafter called “FL wheel speed difference”), the difference between the actually measured value and the estimated value of the RL (rear right wheel) wheel speed (hereinafter called “RL wheel speed difference”), pedal operation by the driver, the longitude/latitude, and the like. In FIG.  9 , selecting logics of road surface input and collision, which use time series data of the instant of an accident and before and after the accident, are considered. 
     In time band  300  of  FIG. 9 , marked fluctuations are exhibited in all of the acceleration difference, the FL wheel speed difference and the RL wheel speed difference. Further, after a marked change is exhibited in the acceleration difference at time point t 1  within the time band  300 , the FL wheel speed difference and the RL wheel speed difference respectively exhibit marked changes simultaneously at time point t 2 . As illustrated in  FIG. 9 , after a change in the acceleration difference, a case in which the difference between the measured value and the estimated value of the wheel speed (hereinafter called “wheel speed difference”) changes, or a case in which the wheel speed differences of the front wheels and the rear wheels or of the respective four wheels change, are cases in which collision due to an accident is surmised. Further, after a change in the wheel speed difference, a case in which the acceleration difference changes, or a case in which the wheel speed differences of the front wheels and the rear wheels change in order, are cases that can be determined to be road surface input. 
     In the present exemplary embodiment, at the time of carrying out machine learning by using LSTM, as illustrated in  FIG. 1 , a large number of instances of road surface input can be collected from a large number of the vehicles  200 . Therefore, the acceleration difference due to road surface input is estimated from driving operations and wheel speed differences, and, by subtracting the acceleration that is due to road surface input from the estimated acceleration difference, acceleration that is due to a collision can be estimated. 
     Further, if displacement in the vertical direction of the position information of the vehicle  200  can be detected by a GPS or the like, the acceleration that relates to the acceleration difference at the time when this displacement is greater than or equal to a predetermined threshold value may be determined to be road surface input. Moreover, because places where there are undulations of the road surface and places where the slope changes sharply can be known, acceleration relating to the acceleration difference at the time when the vehicle  200  passes over such a place may be determined to be road surface input. Moreover, in a case in which similar wheel speed fluctuations and acceleration fluctuations arise at the same place at the plural vehicles  200 , information of these places may be made into a database, and the acceleration that is detected at the time when the vehicle  200  passes over such a place may be determined to be road surface input. 
       FIG. 10  is a schematic drawing at the time of carrying out accident determination on the basis of the differences between actually measured values and estimated values estimated by an RNN. The accelerations that can be estimated by the acceleration estimating models f fb (x t ), f lr (x t ) that are constructed by an RNN are accelerations due to driving operations. Therefore, accelerations that cannot be estimated by the acceleration estimating models f fb (x t ), f lr (x t ) are accelerations that are not due to driver operation, and are inferred to be acceleration that is due to a collision of the vehicle  200 . Accordingly, if the differences Δa fb,t , Δa lr,t  between actually measured values and estimated values estimated by an RNN are large, it is thought that acceleration that cannot be estimated by the acceleration estimating models f fb (x t ), f lr (x t ) has arisen. 
     In the present exemplary embodiment, accident determination is carried out on the basis of the differences Δa fb,t , Δa lr,t  between the actually measured values and the estimated values of the accelerations of the vehicle  200 , but acceleration due to road surface input is included in the acceleration that is applied to the vehicle  200 . In the present exemplary embodiment, in addition to estimating acceleration of the vehicle  200  by the acceleration estimating model constructed by LSTM 1 , wheel speeds of the vehicle  200  are estimated for the respective four wheels that are the front and rear wheels by the wheel speed estimating model constructed by LSTM 2 , and wheel speed differences Δv vx,t  are derived, and further, road surface input is estimated by the road surface input estimating model constructed by LSTM 3 , and final accident determination is carried out. The wheel speed difference Δv vx,t  may be derived by using a bypass filter, instead of a model constructed by an RNN. Further, accident determination may be carried out on the basis of differences between actually measured values and estimated values for each of the vertical acceleration, yaw rate, and vehicle speed of the vehicle  200 . 
     An example of processings in the collision sensing thread is described on the basis of the above explanation. In step  700  of the collision sensing thread, collection of various traveling data from the data storage  120  is carried out individually for each of the vehicles  200 . Among the various traveling data of the vehicles  200 , there are data that are needed for the pre-processing that derives the accumulated values and the average values and the like, and therefore, this data is processed in step  700 . 
     In step  702 , data selection that selects various traveling data that are to be used in the acceleration estimation of the vehicle  200 , is carried out. The various traveling data that are used in acceleration estimation are observed amounts relating to changes in the acceleration of the vehicle  200  due to driving operations, and include, for example: measured values of vehicle motions that are mechanically related to the longitudinal and lateral accelerations and the wheel speeds of the vehicle  200 , beginning with the vehicle speed; operation states of vehicle motion control that are causes of increasing/decreasing the longitudinal and lateral accelerations and the wheel speeds, beginning with ABS; control signals that express driving operations by the driver that are causes of increasing/decreasing the longitudinal and lateral accelerations and the wheel speeds, such as the pedals and the steering wheel and the like of the vehicle  200 ; control signals that express operation of power units that are causes of increasing/decreasing the longitudinal and lateral accelerations and the wheel speeds, such as the engine and the like; and the like. In addition, measured values of the environment at the exterior of the vehicle that indirectly affect increasing/decreasing of the longitudinal and lateral accelerations and the wheel speeds such as the external air temperature and the wipers and the like, and control signals expressing operations of onboard equipment that are used depending on vehicle exterior environment, may be used. Data of the exterior that is generally disclosed by a meteorological agency or the like may be used as the measured values of the vehicle exterior environment, rather than values obtained by onboard sensors. In the data selection of step  602 , various traveling data of the vehicles  200  under a broad range of conditions such as geographical regions or weather conditions or the like, are collected as the teaching data. Further, marked changes in the acceleration due to sudden braking or the like of the vehicle  200 , extreme driving operations such as sudden turning of the steering wheel or the like, extreme motion states of the vehicle  200  such as high speeds or the like, control states that arise infrequently such as ABS operation, and the like are employed as much as possible. 
     In step  702 , the speed of processing may be increased by filtering the various traveling data by a logic that is more simple than an RNN, and reducing the amount of processing of the stage thereafter. For example, as compared with using jerk, which is the time derivative of acceleration, as the predetermined threshold value, the system may be configured such that data in which jerk is less than a predetermined threshold value is not employed as significant data. 
     In step  704 , the estimated value of the acceleration of the vehicle  200  is derived by using the acceleration estimating models f fb (x t ), f lr (x t ) that were constructed by the model constructing thread. 
     In step  706 , the respective differences Δa fb,t , Δa lr,t  between the actually measured values of the accelerations that were detected by the IMU  26  and the estimated values of the accelerations that were estimated by using the acceleration estimating models f fb (x t ), f lr (x t ) are derived as follows. 
       Δ a   fb,t   =a   fb,t   −â   fb,t  
 
       Δ a   lr,t   =a   lr,t   −â   lr,t  
 
     As described above, in the present exemplary embodiment, acceleration that cannot be estimated by the acceleration estimating models f fb (x t ), f lr (x t ) that are constructed by an RNN using LSTM are considered to be force from the vehicle exterior. Accordingly, a case in which either of the differences Δa fb,t , Δa lr,t  is greater than or equal to a predetermined threshold value is a case in which a large acceleration has been applied to the vehicle  200  from the vehicle exterior, and it can be inferred that an accident due to a collision has occurred. The predetermined threshold value is determined on the basis of the various traveling data that are collected from the plural vehicles  200 . Further, a predetermined threshold value may be used in common for the differences Δa fb,t , Δa lr,t  respectively, or there may be different values for the differences Δa fb,t , Δa lr,t  respectively. 
       FIG. 11  is a schematic drawing illustrating an example of values detected by various sensors. In  FIG. 11 , actually measured values (the solid lines) that are measured by sensors such as the IMU  26  and the like, and estimated values (the broken lines) that are estimated by the acceleration estimating models f fb (x t ), f lr (x t ), are illustrated for the longitudinal acceleration and the lateral acceleration. 
     In  FIG. 11 , there are cases in which the actually measured values and the estimated values in the lateral acceleration diverge greatly, and, in such cases, there is the possibility of a collision. In step  708 , in a case in which either of the differences Δa fb,t , Δa lr,t  is greater than or equal to a predetermined threshold value, it is determined that the vehicle  200  has collided. The predetermined threshold value is determined on the basis of statistics of actually measured values of behaviors of the vehicles  200 . Further, as needed, an estimated input composite value at from the vehicle exterior may be derived by using the following formula, and, in a case in which the estimated input composite value at exceeds a predetermined composite value upper limit, it may be inferred that the vehicle  200  has collided. 
         a   t =√{square root over ((Δ a   fb,t ) 2 +(Δ a   lr,t ) 2 )}
 
     In step  710 , the respective wheel speeds of the four wheels that are the front and rear wheels of the vehicle  200  are estimated by using the wheel speed estimating model that was constructed by LSTM  2 . 
     In step  712 , acceleration relating to road surface input is estimated by using the road surface input estimating model constructed by LSTM  2 . In step  712 , for example, the wheel speed difference of the vehicle  200  is derived. Then, after a change in the wheel speed difference, in a case in which the acceleration difference derived in step  706  changes, or in a case in which the wheel speed differences of the front wheels and rear wheels change in order, that acceleration difference is considered to be due to road surface input. Further, after a change in the acceleration difference, in a case in which the wheel speed difference changes, or in a case in which the wheel speed differences of the front wheels and the rear wheels or of the respective four wheels fluctuate simultaneously, that acceleration difference is considered to be due to a collision. 
       FIG. 12A  is an example of an image in a case in which the vehicle  200  starts to ride-up over a step  310 .  FIG. 12B  is an explanatory drawing illustrating a case in which, at the time when the vehicle  200  rides-up over the step  310 , the left and right front wheels rotate idly and the speeds thereof increase, and thereafter, the left and right rear wheels rotate idly and the speeds thereof increase. In  FIG. 12B , the solid lines are actually measured values, and the dotted lines are estimated values. Because  FIG. 12B  is a case in which the wheel speed differences of the front wheels and rear wheels change in order, it can be inferred that the acceleration difference arises due to road surface input. 
       FIG. 13A  is an example of an image in a case in which the vehicle  200  passes over a rough road surface  320 .  FIG. 13B  is an explanatory drawing illustrating a case in which two maximum values of the acceleration difference are observed, and the speeds of front left wheel and the rear left wheel respectively increase immediately before the two maximum values respectively. In  FIG. 13B , the solid lines are actually measured values, and the dotted lines are estimated values.  FIG. 13B  is a case in which, after a change in the wheel speed difference, the acceleration difference changes, and the wheel speed differences of the front wheels and the rear wheels change in order, and therefore, it can be inferred that the acceleration difference arises due to road surface input. 
     Acceleration that is not due to driving operations can be detected by image analysis as well.  FIG. 14A  is an example of an image that is acquired by the imaging device  22  of the vehicle  200  and is of a case in which the front right wheel of the vehicle  200  rotates idly at the time when the driver depresses the accelerator pedal at the time of making a right turn over the step between a roadway and a sidewalk.  FIG. 14B  is an explanatory drawing illustrating a case in which, as a result of the front right wheel of the vehicle  200  rotating idly and the speed thereof increasing, divergence arises between the actually measured value (the solid line) and the estimated value (the dotted line) of the wheel speed at the front right wheel. In  FIG. 14A , the front right wheel of the vehicle  200  floats-up such that there is so-called three point road contact, and the front right wheel rotates idly. 
       FIG. 15A  is an example of an image that is acquired by the imaging device  22  of the vehicle  200  and is of a case in which unloading (pitching) of the rear left wheel of the vehicle  200  arises.  FIG. 15B  is explanatory drawing illustrating a case in which, as a result of the rear left wheel rotating idly, divergence arises between the actually measured value (the solid line) and the estimated value (the dotted line) of the wheel speed at the rear left wheel. The case illustrated in  FIG. 15A  and  FIG. 15B  is a case in which the driver of the vehicle  200  sees a person  210  who is riding a bicycle ahead, and carries out sudden braking and steering toward the left. As a result, the rear left wheel slips, the vehicle  200  decelerates due to the braking force of the brakes, and a difference arises between the actually measured value and the estimated value of the wheel speed of the rear left wheel. 
     In all of the cases of  FIG. 14A ,  FIG. 14B ,  FIG. 15A  and  FIG. 15B , acceleration that is not caused by a driving operation may arise even though the vehicle  200  has not collided. In step  712 , in addition to road surface input estimation, accident determination may be carried out by, for example, sudden behavior that is other than a collision of the vehicle  200  being extracted from the time series image data acquired by the imaging device  22 , and the marked change in the actually measured value of the acceleration at the time when that behavior was effectuated being considered to be acceleration that does not relate to a collision and being excluded from the actually measured values. In the extracting of sudden behavior that is other than a collision of the vehicle  200  from image data, as an example, determination is carried out on the basis of positional changes per unit time in the image data of pixels (pixels of note) that show specific places, such as the four corners or the like, of the vehicle  200 . In a case in which the positional change per unit time of pixels of note is greater than or equal to a predetermined positional change threshold value, it is determined that there is a sudden behavior of the vehicle  200 . Moreover, if there no change in the shape of or the surface area of the set of pixels that show the vehicle  200  in the image data, it can be determined that there is sudden behavior that is other than a collision of the vehicle  200 . 
     In step  714 , in a case in which the acceleration difference is due to road surface input, processing is ended without using the acceleration that relates to this acceleration difference in the collision determination. In step  714 , if the acceleration difference is not due to road surface input, processing moves on to step  716 . 
     In step  716 , the damage direction of the vehicle  200  is estimated by using the following formula. Given that the difference Δa fb,t  is the vector amount in the longitudinal direction and Δa lr,t  is the vector amount in the lateral direction, the quotient of the difference Δa fb,t  and the difference Δa lr,t  is the tangent of the angle expressing the damage direction. Accordingly, inverse tangent d t  of the quotient of the difference Δa fb,t  and the difference Δa lr,t  is the angle expressing the damage direction. The damage direction may be estimated on the basis of, other than the direction of the acceleration, the displacement of the position information of the vehicle  200  detected by the GPS, or the changes in the respective wheel speeds of the vehicle  200 . 
     
       
         
           
             
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     In step  718 , the computing server  10  notifies the business terminal  130  or the like of the results of determination, and ends the processing. 
       FIG. 16  is a block drawing illustrating an example of employing the information processing device  100  relating to the present exemplary embodiment. As illustrated in  FIG. 16 , the data storage  120  collects various traveling data of the vehicle  200  as in steps  600 ,  700  of  FIG. 8 . 
     The various traveling data of the vehicle  200  that are acquired by the data storage  120  are transmitted to the computing server, and selecting of the data is carried out as in steps  602 ,  702  of  FIG. 8 . 
     At the computing server  10 , learning such as in step  604  of  FIG. 8  is carried out, and the acceleration estimating models f fb (x t ), f lr (x t ) are constructed. Then, by using the acceleration estimating models f fb (x t ), f lr (x t ), acceleration estimation, deriving of the differences Δa fb,t , Δa lr,t , collision determining, and estimating of the damage direction that are illustrated in steps  704  through  710  of  FIG. 8  are carried out. 
     Then, as in step  712  of  FIG. 8 , the computing server  10  notifies the business terminal  130  of the assessment of a used vehicle, maintenance of the vehicle  200 , inspection of the vehicle  200 , a record of operations of the vehicle  200 , new car sales business, safety confirming calls, and the like. 
     In the employment example that is illustrated in  FIG. 16 , at the computing server  10 , data collection and selecting, construction of the acceleration estimating models f fb (x t ), f lr (x t ), acceleration estimation, deriving of the differences Δa fb,t , Δa lr,t , collision determining, and estimating of the damage direction are carried out. However, in order to reduce the load, the computing server  10  may carry out constructing of the acceleration estimating models f fb (x t ), f lr (x t ) from the various traveling data of the vehicle  200  acquired from the data storage  120 , and, at another server, the data collection and selecting, the acceleration estimating, the deriving of the differences Δa fb,t , Δa lr,t , the collision determining, and the estimating of the damage direction relating to steps  700  through  710  of  FIG. 6  may be carried out by using the acceleration estimating models f fb (x t ), f lr (x t ) that were constructed at the computing server  10 . 
       FIG. 17  is a block drawing illustrating another example of employment of the information processing device  100  relating to the present exemplary embodiment. As illustrated in  FIG. 17 , the data storage  120  collects various traveling data of the vehicle  200  as in step  600  of  FIG. 8 . 
     The various traveling data of the vehicle  200  that are acquired by the data storage  120  are transmitted to the computing server, and selecting of the data is carried out as in step  602  of  FIG. 8 . 
     At the computing server  10 , learning such as in step  604  of  FIG. 8  is carried out, and the acceleration estimating models f fb (x t ), f lr (x t ) are constructed. Then, the constructed acceleration estimating models f fb (x t ), f lr (x t ) are transmitted to the vehicle  200 . 
     At the vehicle  200 , the selecting of data, the acceleration estimation, the deriving of the differences Δa fb,t , Δa lr,t , the collision determining, and the estimating of the damage direction that are illustrated in steps  702  through  710  of  FIG. 8  are carried out by the computing device  14 . 
     Then, as in step  712  of  FIG. 8 , the vehicle  200  notifies the business terminal  130  of the assessment of a used vehicle, maintenance of the vehicle  200 , inspection of the vehicle  200 , a record of operations of the vehicle  200 , new car sales business, safety confirming calls, and the like. 
     In the employment example that is illustrated in  FIG. 17 , because the collision sensing is carried out at the vehicle  200 , there is no need for the data storage  120  to acquire the various traveling data from the vehicle  200  and transfer the acquired various traveling data to the computing server  10 , and therefore, the capacity of the data storage  120  can be reduced. 
     As described above, in the present exemplary embodiment, it is determined that the vehicle  200  has collided in a case in which the difference between an actually measured value of the acceleration of the vehicle  200  that is detected at the IMU  26  or the like, and an estimated value of the acceleration of the vehicle  200  that is derived on the basis of an observed amount relating to a change in the acceleration of the vehicle  200  due to a driving operation, is greater than or equal to a predetermined threshold value. 
     The estimated value of the acceleration, which is derived on the basis of an observed amount relating to a change in the acceleration of the vehicle due to a driving operation, is an estimated value of the acceleration of the vehicle  200  due to a driving operation. Further, the acceleration of the vehicle  200  that arises at the time of a collision is different than a change in acceleration that is due to a driving operation. Accordingly, it is determined that the vehicle  200  has collided in a case in which the difference between an actually measured value of the acceleration of the vehicle  200  that is detected at the IMU  26  or the like, and an estimated value of the acceleration of the vehicle  200  that is derived on the basis of an observed amount relating to a change in the acceleration of the vehicle  200  due to a driving operation, is greater than or equal to a predetermined threshold value. 
     Even in a light collision, divergence can arise between the actually measured value and the predicted value of the acceleration, and therefore, a collision of the vehicle  200  can be inferred accurately. 
     In the present exemplary embodiment, individual acceleration estimating models may be constructed for each configuration of the vehicle  200 , such as per vehicle type, year model, grade, tires that are used and the like, and further, models may be constructed in accordance with the country, season and weather in which the vehicle  200  is used. Or, a candidate model for acceleration estimation may be constructed by adding, to the above-described various traveling data, conditions relating to configurations of the vehicle  200  such as the vehicle type, the year model, the grade, the tires that are used and the like, and the country, season and weather in which the vehicle  200  is used. The accuracy of collision sensing can be improved by constructing the acceleration estimating model by subdividing the conditions. 
     Further, because the vehicle  200  that is a connected car is used in collecting the various traveling data, collision sensing can be executed at the computing server  10  or the like that exists remotely from the vehicle  200 , without requiring the retrofitting of devices to the vehicle  200 . 
     In the present exemplary embodiment, the acceleration estimating model is constructed by machine learning that is based on so-called big data that is acquired from a large number of the vehicles  200 . Therefore, a model that can accurately estimate the acceleration of the vehicle  200  due to driving operations can be constructed. 
     In the present exemplary embodiment, the model f fb (x t ) that estimates the longitudinal acceleration of the vehicle  200  and the model f lr (x t ) that estimates the lateral acceleration of the vehicle  200  are constructed, and the damage direction of the vehicle  200  can be estimated from the predicted value of the acceleration in the longitudinal direction and the acceleration in the lateral direction of the vehicle  200 . Moreover, the results of inferring a collision accident and the estimated results of the damage direction of the vehicle  200  respectively can be useful in: describing the accident history in the sale of a used vehicle; guidelines for maintenance of business vehicles such as taxis and the like; estimating the absence/presence of a collision when a rental car is returned; providing notifications that the vehicle should be brought to a dealer or a repair shop; safety confirming notification from an insurance company in a case in which an accident is surmised; records of operations of autonomous vehicles; and the like. 
     Moreover, in the present exemplary embodiment, the wheel speeds of the vehicle  200  are estimated, and, from the form of the difference between the actually measured value and the estimated value of the wheel speed, the absence/presence of acceleration due to road surface input can be determined, and, from this determination, the accuracy of sensing a collision can be improved. 
     Note that the “detecting section” in the claims corresponds respectively to the “imaging device  22 ”, the “vehicle speed sensor  24 ”, the “steering angle sensor  28 ”, the “throttle sensor  30 ” and the “brake pedal sensor  32 ” of the detailed description of the specification. Further, the “inertial measurement section” in the claims corresponds to the “IMU  26 ” of the detailed description of the specification. 
     Note that any of various types of processors other than a CPU may execute the processings that are executed by the CPU reading-in software (programs) in the above-described exemplary embodiment. Examples of processors in this case include PLDs (Programmable Logic Devices) whose circuit configuration can be changed after production such as FPGAs (Field-Programmable Gate Arrays) and the like, and dedicated electrical circuits that are processors having circuit configurations that are designed for the sole purpose of executing specific processings such as ASICs (Application Specific Integrated Circuits) and the like, and the like. Further, the processings may be executed by one of these various types of processors, or may be executed by a combination of two or more of the same type or different types of processors (e.g., plural FPGAs, or a combination of a CPU and an FPGA, or the like). Further, the hardware configurations of these various types of processors are, more concretely, electrical circuits that combine circuit elements such as semiconductor elements and the like. 
     Further, the above exemplary embodiment describes an aspect in which the programs are stored in advance (are installed) in the disk drive  60  or the like, but the present disclosure is not limited to this. The programs may be provided in a form of being stored on a non-transitory storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), a USB (Universal Serial Bus) memory, or the like. Further, the programs may be in forms of being downloaded from an external device via a network. 
     (Supplementary Note 1) 
     An information processing device is configured to include: 
     a memory; and 
     at least one processor coupled to the memory, the least one processor being configured to: 
     determine that a vehicle has collided, in a case in which a difference between, an actually measured value of acceleration of the vehicle that is detected by an inertial measurement section, and an estimated value of acceleration of the vehicle that is derived on the basis of an observed amount relating to a change in acceleration of the vehicle due to a driving operation detected by a detecting section, is greater than or equal to a predetermined threshold value.