JP2018120291A - Event prediction system, event prediction method, program, and moving body - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an event prediction system which can predict occurrence of an event caused by an object hidden from a driver, an event prediction method, a program, and a moving body.SOLUTION: An event prediction system 1 first acquisition unit 111, second acquisition unit 112, and estimation unit 113. The first acquisition unit 111 acquires time information indicating the time required before a prediction time point when occurence of an event relating to driving of a moving body 100 is predicted. The second acquisition unit 112 acquires moving-body information representing a state of the moving body 100. The estimation unit 113 estimates a location where the event occurs, on the basis of the time information and the moving-body information.SELECTED DRAWING: Figure 1

Description

  The present invention generally relates to an event prediction system, an event prediction method, a program, and a moving body, and more particularly, an event prediction system, an event prediction method, a program, and a movement for predicting occurrence of an event related to driving of the moving body. About the body.

  2. Description of the Related Art Conventionally, a driving support device that supports driving of a vehicle by performing a risk prediction of the host vehicle and notifying a driver of a prediction result is known (for example, see Patent Document 1).

  The driving support apparatus described in Patent Literature 1 includes a driving capability confirmation unit, a risk prediction unit, and a display control unit. The driving ability confirmation unit periodically performs a driving skill test based on the detection information of the environment information acquisition unit, the own vehicle information acquisition unit, and the driver information acquisition unit, determines the driving behavior of the driver from the test result, Check driving ability. The risk prediction unit performs a risk prediction of the host vehicle based on the determination result of the driving behavior of the driver. The display control unit predicts the future position of the host vehicle based on the detection information of the environment information acquisition unit, the host vehicle information acquisition unit, and the driver information acquisition unit, and corresponds the future position of the host vehicle to the collision risk of the host vehicle. It is displayed on the display unit in the display mode.

JP 2012-128655 A

  However, in the driving support device described in Patent Document 1, since the driver (driver) is notified of the possibility of a collision by displaying the prediction result of the future position of the host vehicle, It is limited to events (accidents, etc.) that occur within the visible range of the person. Therefore, in the driving support apparatus described in Patent Document 1, for example, an event caused by an object (in this case, a pedestrian) that becomes a blind spot of the driver, such as a pedestrian jumping out from behind a vehicle parked on the road Cannot be predicted.

  The present invention has been made in view of the above-described reasons, and an object thereof is to provide an event prediction system, an event prediction method, a program, and a moving body that can predict the occurrence of an event caused by an object that becomes a blind spot of a driver. And

  The event prediction system according to the first aspect includes a first acquisition unit, a second acquisition unit, and an estimation unit. The first acquisition unit acquires time information representing a required time until a predicted time point at which an event related to driving of the moving object is predicted to occur. The second acquisition unit acquires mobile body information representing the status of the mobile body. The estimation unit estimates an occurrence location of the event based on the time information and the moving body information.

  In the event prediction system according to a second aspect, in the first aspect, the moving body information includes information on an object around the moving body, information on the state of the moving body, and information on the position of the moving body. And at least one of.

  In the event prediction system according to a third aspect, in the first or second aspect, the estimation unit predicts the arrival position of the mobile body at the prediction time point based on the time information and the mobile body information. It is configured. The estimation unit is configured to estimate the occurrence location of the event from the arrival position.

  The event prediction system according to a fourth aspect is the event prediction system according to the third aspect, wherein the estimator is located around the moving body, and an object existing at the arrival position after the required time is generated. It is comprised so that it may estimate as a location.

  In any one of the first to fourth aspects, the event prediction system according to the fifth aspect further includes a notification unit that notifies the occurrence location of the event estimated by the estimation unit.

  In the event prediction system according to a sixth aspect, in the fifth aspect, the notification unit includes a display unit that notifies by displaying the occurrence location of the event.

  In any one of the first to sixth aspects, the event prediction system according to a seventh aspect further includes a time prediction unit that predicts the required time. The said time prediction part is comprised so that the said required time may be estimated using the information for a prediction regarding the said mobile body, and a prediction model.

  In an event prediction system according to an eighth aspect, in the seventh aspect, the time prediction unit is configured to use a different prediction model for each attribute of a driver who drives the mobile body.

  The event prediction system according to the ninth aspect includes an accumulation unit and a generation unit. The accumulating unit accumulates a plurality of learning data including history information indicating the state of the moving body when an event related to driving of the moving body occurs. The generation unit generates a prediction model for predicting a required time to a prediction time point at which the occurrence of the event is predicted, using the plurality of learning data. Each of the plurality of learning data further includes label information indicating a time required until the event occurs.

  The event prediction method according to the tenth aspect includes a first acquisition process, a second acquisition process, and an estimation process. The first acquisition process acquires time information representing a required time until a predicted time point at which an event related to driving of the moving object is predicted to occur. In the second acquisition process, mobile object information representing the status of the mobile object is acquired. The estimation process estimates the occurrence location of the event based on the time information and the moving body information.

  A program according to an eleventh aspect is a program for causing a computer system to execute a first acquisition process, a second acquisition process, and an estimation process. The first acquisition process acquires time information representing a required time until a predicted time point at which an event related to driving of the moving object is predicted to occur. In the second acquisition process, mobile object information representing the status of the mobile object is acquired. The estimation process estimates the occurrence location of the event based on the time information and the moving body information.

  A mobile body according to a twelfth aspect includes the event prediction system according to any one of the first to ninth aspects.

  The present invention has an advantage that it is possible to predict the occurrence of an event caused by an object that becomes a driver's blind spot.

FIG. 1 is a block diagram illustrating a configuration of an event prediction system according to the first embodiment. FIG. 2 is a flowchart showing an operation related to generation of a prediction model of the event prediction system described above. FIG. 3 is a flowchart showing an operation related to estimation of an event occurrence point of the event prediction system same as above. FIG. 4 is a conceptual diagram showing a driver's field of view when the event prediction system is used. 5A to 5C are conceptual diagrams showing examples of events that can be predicted by the event prediction system described above. 6A to 6C are conceptual diagrams showing other examples of events that can be predicted by the event prediction system described above.

(Embodiment 1)
(1) Overview The event prediction system 1 (see FIG. 1) according to the present embodiment is a system for predicting the occurrence of an event related to the driving of a moving body 100 (see FIG. 1) such as an automobile. In this embodiment, the case where the moving body 100 to which the event prediction system 1 is applied is an automobile will be described as an example.

  The “event” here means, for example, an event that the driver of the moving body 100 feels dangerous when driving. This type of “event” is directly related to accidents such as collisions between vehicles, collisions of vehicles with structures such as guardrails, and contact between pedestrians and vehicles, or accidents that do not lead to accidents. There is an event that is likely to occur (so-called near-miss). Further, the “event occurrence location” here means a location where an event occurs, a location (point) where an event such as an intersection or a pedestrian crossing occurs, a vehicle around the mobile object 100, walking It includes both a specific object (part) that is a target of an event such as a person or a small animal.

  The event prediction system 1 according to the present embodiment mainly predicts the occurrence of an event caused by an object that becomes a driver's blind spot. Specific examples of this type of event include a pedestrian jumping out from behind a vehicle parked on the road and the appearance of a vehicle going straight from behind a vehicle waiting for a right turn (or left turn). This type of event that occurs outside the driver's visible range is also called "invisible danger". The driver of the moving body 100 usually determines the event of this kind (invisible danger) based on the situation of the moving body 100, that is, based on the situation in which the moving body 100 is placed. Forecasts based on the experience of That is, in general, the driver can predict the “invisible danger” to some extent by experiencing the driving of the moving body 100 in various situations. For this reason, the predictability of “invisible danger” may vary greatly depending on the driving skill of the driver, the driving sense, and the driver's state (including the driver's mental state).

  According to the event prediction system 1, since this type of event (invisible danger) is mainly predicted, the prediction of “invisible danger” based on the driving skill of the driver, the sense of driving, and the state of the driver, etc. It is possible to suppress the variation in the characteristics. Therefore, for example, even a driver who has relatively little driving experience can perform driving considering the possibility of this type of event. Furthermore, for example, even when the driver is in a state of reduced concentration compared to normal times due to fatigue, lack of sleep, etc., the driver can drive considering the possibility of this type of event occurring. Become. In addition, an event may occur when the driver may be late in noticing the event, such as when the driver is just looking away or the driver is distracted. It becomes possible to notice the sex quickly, and safer driving becomes possible. In short, according to the event prediction system 1 that can also predict the occurrence of an event caused by an object that becomes a blind spot of the driver, the driver can support the driving of the moving body 100 so as to realize safer driving. It becomes.

  The event prediction system 1 according to the present embodiment predicts the occurrence of an event using, for example, a prediction model generated by a machine learning algorithm from history information indicating the situation of the mobile object 100 when the event actually occurs. To do. That is, a prediction model generated from history information or the like representing the state of the moving body 100 makes it possible to predict the occurrence of an event instead of the driving experience of the driver. For example, from various situations of the moving body 100 such as what kind of object exists around the moving body 100, what speed the moving body 100 moves, and what place the moving body 100 is moving. It is possible to predict the occurrence of an event.

  The event prediction result in the event prediction system 1 is preferably notified to the driver by, for example, being displayed on a head-up display (HUD), a multi-information display, or the like. As a result, when the occurrence of an event caused by an object that becomes a driver's blind spot is predicted, the driver is informed of this, for example, a driver who has relatively little driving experience. However, it is possible to drive in consideration of the possibility of this type of event. Furthermore, for example, even when the driver is in a state of reduced concentration compared to normal times due to fatigue, lack of sleep, etc., the driver can drive considering the possibility of this type of event occurring. Become. In addition, an event may occur when the driver may be late in noticing the event, such as when the driver is just looking away or the driver is distracted. It becomes possible to notice the sex quickly, and safer driving becomes possible.

(2) Configuration As shown in FIG. 1, the event prediction system 1 according to the present embodiment is implemented in a prediction block 11 implemented in a moving body 100 (automobile in this embodiment) and a cloud 200 (cloud computing). Learning block 12.

  The event prediction system 1 further includes a notification unit 13 mounted on the moving body 100. The event prediction system 1 further includes an ADAS information input unit 14, a vehicle information input unit 15, and a position information input unit 16 that are mounted on the moving body 100.

  The prediction block 11 and the learning block 12 are configured to be communicable. Since the prediction block 11 is implemented in the mobile unit 100, communication with the learning block 12 implemented in the cloud 200 is, for example, a mobile phone network (carrier network) provided by a communication carrier, and the public such as the Internet. This is done via the network. Examples of the mobile phone network include a 3G (third generation) line and an LTE (Long Term Evolution) line. The prediction block 11 may be configured to be able to communicate with the learning block 12 via a public wireless LAN (Local Area Network).

  The prediction block 11 includes a first acquisition unit 111, a second acquisition unit 112, an estimation unit 113, a time prediction unit 114, a model storage unit 115, an input information processing unit 116, an output information processing unit 117, have. The prediction block 11 is composed of, for example, a computer system having a central processing unit (CPU) and a memory as main components, and the computer system executes the program stored in the memory so that the computer system can execute the prediction block 11. Function as. The program is recorded in advance in the memory of the prediction block 11 here, but may be provided through a telecommunication line such as the Internet or recorded in a recording medium such as a memory card.

  The first acquisition unit 111 acquires time information. The “time information” here is information that represents a required time to a predicted time point at which an event related to the operation of the moving body 100 is predicted to occur. In other words, when the occurrence of an accident such as an accident while driving the mobile unit 100 or a near-miss event is predicted, the required time to the predicted time when the predicted time of occurrence of this event is the “predicted time” Time is represented by time information. For example, when the occurrence of an event is predicted 3 seconds after the current time, that is, when the predicted time is 3 seconds after the current time, the time required to reach the predicted time is “3 seconds”. "

  The second acquisition unit 112 acquires moving body information. The “mobile body information” here is information representing the status of the mobile body 100. In the present embodiment, the mobile object information includes information related to objects around the mobile object 100 (also referred to as “ADAS information”), information related to the state of the mobile object 100 (also referred to as “vehicle information”), Information on the position (also referred to as “position information”).

  The ADAS information is information that can be detected by a camera, sonar sensor, radar, LiDAR (Light Detection and Ranging), or the like, which is a detection unit of an advanced driver assistance system (ADAS). Specific examples of ADAS information include the distance from the moving body 100 to a vehicle traveling around the moving body 100, the relative coordinates of the vehicle with respect to the moving body 100, the inter-vehicle distances between a plurality of vehicles, and the There are relative speeds. Here, the objects in the vicinity of the moving body 100 in the ADAS information include a pedestrian, a small animal, and the like in addition to structures such as a vehicle and a guardrail that are running or stopped around the moving body 100.

  The vehicle information is information that represents the local state of the moving body 100 itself and is information that can be detected by a sensor mounted on the moving body 100. Specific examples of the vehicle information include a moving speed (traveling speed) of the moving body 100, an acceleration applied to the moving body 100, an accelerator pedal depression amount (accelerator opening), a brake pedal depression amount, a steering angle, and a driver monitor. There are detected driver's pulse, facial expression, line of sight, and the like. Furthermore, data unique to the moving body 100 such as the vehicle width, the vehicle height, the total length, and the eye point is also included in the vehicle information.

  The position information is information based on the position of the moving body 100 and is information that can be detected using GPS (Global Positioning System) such as road information at the position of the vehicle. Specific examples of location information include the number of lanes of the road at the location of the vehicle, whether it is an intersection, whether it is a street, whether it is one-way, the width of a roadway, the presence or absence of a sidewalk, a gradient, and the curvature of a curve. .

  Each specific example of ADAS information, vehicle information, and position information is not limited to the above-described example. For example, when the driver monitor can detect the driver's face direction, drowsiness level, emotion, and the like, these pieces of information (driver's face direction, drowsiness level, emotion, etc.) are also included in the vehicle information. included.

  The estimation unit 113 estimates the occurrence location of the event based on the time information and the moving body information. That is, the estimation unit 113 estimates the occurrence location of an event that is predicted to occur based on the time information acquired by the first acquisition unit 111 and the moving body information acquired by the second acquisition unit 112.

  Here, the estimation unit 113 is configured to predict the arrival position of the moving body 100 at the prediction time point based on the time information and the moving body information, and to estimate the occurrence location of the event from the predicted arrival position. . That is, the estimation unit 113 first predicts the arrival position of the moving body 100 at the time point when the occurrence of the event is predicted (predicted time point) from the time information. And the estimation part 113 estimates the generation | occurrence | production location of an event from the arrival position.

  Furthermore, the estimation unit 113 is configured to estimate an object that exists in the vicinity of the moving body 100 and exists at the arrival position after a required time as an event occurrence location. As described above, the “event occurrence location” is a place where an event such as an intersection or a pedestrian crossing occurs, and a specific object that is a target of an event such as a vehicle, a pedestrian, or a small animal around the moving body 100. Although both are included, in this embodiment, the latter (specific object) is set as an estimation target. Specific processing of the estimation unit 113 will be described in the column “(3.2) Prediction operation”.

  The time prediction unit 114 predicts the required time. In the present embodiment, the time prediction unit 114 is configured to predict a required time to a prediction time point at which an occurrence of an event is predicted, using prediction information regarding the mobile object 100 and a prediction model. . Here, the information for prediction is information representing the status of the mobile object 100 and is the same information as the mobile object information acquired by the second acquisition unit 112. The “prediction model” here is a learned model that is generated by a machine learning algorithm from history information or the like representing the state of the moving object 100 when an event actually occurs in the learning block 12. That is, the time information representing the time required to the prediction time point acquired by the first acquisition unit 111 is information that the prediction block 11 does not acquire from the outside, but is generated by the time prediction unit 114 using the prediction model. is there.

  In addition, the time prediction unit 114 is configured to transmit history information representing the state of the moving object 100 when an event occurs to the learning block 12. The “history information” here is information representing the status of the moving body 100 and the same information as the moving body information (and prediction information) acquired by the second acquisition unit 112. However, the time prediction unit 114 does not always transmit history information to the learning block 12 but transmits history information to the learning block 12 only when an event occurs. Whether or not an event has occurred is detected by the detection results of the sonar sensor and radar, the operating condition of the airbag, the detection results of the sudden brake and the sudden handle, or the driver's pulse and facial expression measured by the driver monitor. Is possible. That is, the prediction block 11 transmits, for example, history information for several seconds before and after the occurrence of the event to the learning block 12 using the occurrence of the event as a trigger. At this time, when history information is acquired at regular time intervals (for example, 0.1 seconds), a plurality of history information acquired in a few seconds before and after the occurrence of the event is collected in the learning block 12. Sent.

  Here, the time prediction unit 114 transmits time information representing a required time until the occurrence of the event to the learning block 12 as the label information together with the history information. Unlike the time information predicted by the time prediction unit 114, the time information as the label information is information representing the actual required time until the occurrence of the event is detected. When a plurality of pieces of history information are transmitted together in a learning block, time information as label information is associated with each of the plurality of pieces of history information. The time information as the label information preferably represents an elapsed time from the event occurrence time in addition to the required time to the event occurrence time. The required time until the event occurrence time and the elapsed time from the event occurrence time are distinguished by, for example, a sign (+/−). As an example, if the required time is “+” and the elapsed time is “−”, 5 seconds before the event occurrence time is represented by “+5 seconds”, and 5 seconds after the event occurrence time is “−5 seconds”. ". As will be described in detail later, the history information and the label information (time information) are used for generating a prediction model in the learning block 12.

  The model storage unit 115 stores a prediction model used for prediction of the required time in the time prediction unit 114. In the present embodiment, the prediction model generated in the learning block 12 is transmitted (distributed) from the learning block 12 to the prediction block 11 through communication between the prediction block 11 and the learning block 12 and stored in the model storage unit 115. (Memorized). In the present embodiment, it is assumed that one prediction model is stored in the model storage unit 115. The model storage unit 115 acquires a new prediction model from the learning block 12 as needed, and updates the stored prediction model as needed. However, the model storage unit 115 may store a plurality of prediction models.

  The input information processing unit 116 is connected to the ADAS information input unit 14, the vehicle information input unit 15, and the position information input unit 16. The ADAS information input unit 14, the vehicle information input unit 15, and the position information input unit 16 are input interfaces for ADAS information, vehicle information, and position information, respectively. Therefore, ADAS information is input from the ADAS information input unit 14 to the input information processing unit 116, vehicle information is input from the vehicle information input unit 15, and position information is input from the position information input unit 16. The input information processing unit 116 outputs ADAS information, vehicle information, and position information to the second acquisition unit 112 as moving body information. Further, the input information processing unit 116 outputs ADAS information, vehicle information, and position information to the time prediction unit 114 as prediction information. That is, in this embodiment, each of the moving body information and the prediction information is information including all of ADAS information, vehicle information, and position information.

  The output information processing unit 117 is connected to the estimation unit 113 and the notification unit 13. The output information processing unit 117 receives the estimation result of the estimation unit 113, that is, the occurrence location of the event estimated by the estimation unit 113. The output information processing unit 117 outputs the occurrence location of the event estimated by the estimation unit 113 to the notification unit 13 and causes the notification unit 13 to notify it. In this embodiment, the alerting | reporting part 13 has a display part which alert | reports by displaying the generation | occurrence | production location of an event. Therefore, the output information processing unit 117 outputs the estimation result of the estimation unit 113 to the notification unit 13 as data in a form that can be displayed on the display unit.

  The notification unit 13 notifies the occurrence location of the event estimated by the estimation unit 113. That is, the notification unit 13 receives the estimation result of the estimation unit 113 from the output information processing unit 117, thereby notifying the occurrence location of the event (displayed in the present embodiment). In this embodiment, the alerting | reporting part 13 has 3D-HUD131, 2D-HUD132, the meter 133, and the multi-information display 134 as an example of a display part. The 3D-HUD 131 and the 2D-HUD 132 project the image from below (dashboard) onto the windshield of the moving body 100, thereby allowing the driver to visually recognize the image reflected by the windshield. In particular, the 3D-HUD 131 can project an image that is visually recognized with depth on the road surface in front of the moving body 100. A specific display mode in the notification unit 13 will be described in the column “(3.2) Prediction operation”.

  The learning block 12 includes a storage unit 121 and a generation unit 122. The learning block 12 is composed of, for example, a computer system having a CPU and a memory as main components, and the computer system functions as the learning block 12 when the CPU executes a program stored in the memory. Here, the program is recorded in advance in the memory of the learning block 12, but may be provided by being recorded through a telecommunication line such as the Internet or a recording medium such as a memory card.

  The accumulating unit 121 accumulates a plurality of learning data including history information indicating the state of the moving object 100 when an event occurs. In the present embodiment, label information (time information) transmitted from the time prediction unit 114 to the learning block 12 is stored in the storage unit 121 as learning data together with history information. That is, each of the plurality of learning data stored in the storage unit 121 includes history information when an event occurs, and time information indicating a required time (or elapsed time from the occurrence time) until the event occurs. , Including.

  As described above, the history information in the state to which the time information (label information) is added is stored as learning data in the storage unit 121 using the occurrence of the event as a trigger. The learning data is accumulated in the accumulation unit 121 every time an event occurs, and a plurality of learning data is accumulated in the accumulation unit 121. Here, the plurality of learning data stored in the storage unit 121 is a learning data set used for generating a prediction model in the generation unit 122. That is, the plurality of learning data forms a learning data set processed into a form suitable for machine learning in the generation unit 122 by performing annotation processing on the history information.

  The generation unit 122 generates a prediction model using a plurality of learning data. The generation unit 122 generates a prediction model by a machine learning algorithm using a certain amount or more of learning data. As described above, the prediction model is a learned model that is used to predict a required time to a prediction time point at which an occurrence of an event is predicted by the time prediction unit 114. The prediction model generated by the generation unit 122 is transmitted from the learning block 12 to the prediction block 11 and stored in the model storage unit 115. Here, the generation unit 122 has a sample for evaluating the prediction model, and transmits the prediction model to the prediction block 11 and stored in the model storage unit 115 every time the evaluation of the prediction model is improved. Update the prediction model.

(3) Operation Next, the operation of the event prediction system 1 according to the present embodiment will be described.

(3.1) Learning Operation First, the operation of the event prediction system 1 relating to the generation of the prediction model in the learning block 12 will be described with reference to the flowchart shown in FIG.

  The learning block 12 acquires history information from the prediction block 11 using the occurrence of an event in the prediction block 11 as a trigger (S11). Further, at this time, the learning block 12 acquires label information (time information) associated with the history information together with the history information. The learning block 12 performs annotation processing for adding the acquired time information to the history information as label information (S12). The learning block 12 uses the history information to which the label information (time information) thus obtained is added as learning data, and stores this learning data in the storage unit 121 (S13).

  The learning block 12 uses the value (for example, the number of bits) representing the increase amount of the accumulated learning data as the accumulated data increase amount, and compares the accumulated data increase amount with the predetermined value Q (S14). If the accumulated data increase amount is equal to or greater than the predetermined value Q (S14: Yes), the learning block 12 generates a prediction model in the generation unit 122 (S15). At this time, the generation unit 122 generates a prediction model by a machine learning algorithm using a plurality of learning data stored in the storage unit 121. The prediction model generated by the generation unit 122 is transmitted from the learning block 12 to the prediction block 11 and stored in the model storage unit 115. On the other hand, if the accumulated data increase amount is less than the predetermined value Q (S14: No), the event prediction system 1 skips step S15 and ends the series of processes in the learning block 12.

  The event prediction system 1 generates a prediction model by repeatedly performing the processes of steps S11 to S15 each time an event occurs in the prediction block 11. The learning block 12 transmits the prediction model to the prediction block 11 and updates the prediction model stored in the model storage unit 115 whenever the evaluation of the prediction model is improved.

  Further, when the event prediction system 1 starts operation, the learning block 12 stores a plurality of learning data in the storage unit 121 in advance, so that the learning block 12 can perform prediction without acquiring history information from the prediction block 11. It is preferable that the model can be generated. The same applies to the prediction model. When the operation of the event prediction system 1 is started, it is preferable that a default prediction model is stored in the learning block 12 and the model storage unit 115 in advance.

  Here, as described above, the time information as the label information preferably represents the elapsed time from the event occurrence time in addition to the required time to the event occurrence time. That is, the learning block 12 adds not only the history information before the occurrence of the event but also the history information after the occurrence of the event by adding label information (time information) indicating the elapsed time from the occurrence of the event. It is preferable to store the data as storage data in the storage unit 121. By using the history information after the occurrence of the event, the identification accuracy at the time of occurrence of the event is improved, and as a result, the evaluation of the prediction model generated by the generation unit 122 is improved. Furthermore, by using the history information after the occurrence of the event, the learning block 12 can verify whether the event has occurred or not. In short, even if the occurrence of an event is detected from the detection result of the sudden braking, the event does not occur if it is later determined from the situation of the moving body 100 that it is not an accident or a near-miss, etc. As a result, the learning data can be discarded. For example, when it is determined that the moving body 100 continues to move (run) as usual after the occurrence of an event is detected from the sudden braking detection result, for example, when the driver's driving is rough. In the learning block 12, the learning data is discarded. Thereby, the quality of the learning data stored in the storage unit 121 is improved.

(3.2) Prediction Operation Next, the operation of the event prediction system 1 relating to the estimation of the event occurrence location in the prediction block 11 will be described with reference to the flowchart shown in FIG.

  The prediction block 11 acquires information for prediction in the time prediction unit 114 (S21). At this time, the ADAS information, the vehicle information, and the position information input from the ADAS information input unit 14, the vehicle information input unit 15, and the position information input unit 16 to the input information processing unit 116 are used as prediction information. Is input. The prediction block 11 uses the acquired prediction information and the prediction model stored in the model storage unit 115 to predict the required time in the time prediction unit 114 (S22). Here, if the situation of the moving body 100 specified from the prediction information is in a situation in which it is determined that an event is likely to occur after 3 seconds, for example, the required time is predicted to be 3 seconds. . The acquisition of the prediction information (S21) and the prediction of the required time (S22) are executed as needed at regular time intervals (for example, 0.1 seconds).

  The prediction block 11 compares the predicted required time with a specified time S (for example, 5 seconds) (S23). If the required time is equal to or shorter than the specified time S (S23: Yes), the prediction block 11 starts a process for estimating the occurrence location of the event. Specifically, the prediction block 11 executes a first acquisition process (S24) in which the first acquisition unit 111 acquires time information indicating a required time until a prediction time point at which an occurrence of an event is predicted. At this time, the first acquisition unit 111 acquires information on the required time predicted by the time prediction unit 114 from the time prediction unit 114 as time information. Furthermore, the prediction block 11 performs the 2nd acquisition process (S25) which acquires the mobile body information showing the condition of the mobile body 100 in the 2nd acquisition part 112. FIG. At this time, the second acquisition unit 112 predicts ADAS information, vehicle information, and position information input from the ADAS information input unit 14, the vehicle information input unit 15, and the position information input unit 16 to the input information processing unit 116. Obtained as business information.

  Then, the prediction block 11 performs the estimation process (S26) which estimates the generation | occurrence | production location of an event in the estimation part 113 based on time information and mobile body information. Specifically, the estimation unit 113 first predicts the arrival position of the mobile object 100 at the prediction time point based on the time information and the mobile object information. If the time required to predict the occurrence of an event is T [s] and the moving speed (traveling speed) of the moving body 100 is V [m / s], the distance to the arrival position D [m] is obtained by “D = VT”. The required time T is specified by time information, and the moving speed of the moving body 100 is specified by vehicle information included in the moving body information.

  And the estimation part 113 exists in the periphery of the mobile body 100, and estimates the object which exists in the arrival position after required time as an event generation | occurrence | production location. Specifically, the estimation unit 113 estimates an event occurrence location using the calculated distance D to the arrival position and information such as ADAS information and position information included in the moving body information.

  As an example, a process for estimating an event occurrence place by the estimation unit 113 in the situation shown in FIG. 4 will be described. FIG. 4 is a conceptual diagram showing the field of view of the driver of the moving body 100. In the example of FIG. 4, it is assumed that each of the traveling lane 501 in which the moving body 100 (own vehicle) is traveling and the opposite lane 502 are two-lane straight roads. In this example, a parked truck 301 exists on the shoulder of the traveling lane 501 on the left front side of the moving body 100. It is assumed that the distance from the moving body 100 to the track 301 is 66 [m].

  In this case, if the required time T is 4 [s] and the moving speed V is 16.67 [m / s], the distance D to the reaching position is 66.68 [m]. Then, based on the information such as the inter-vehicle distance and the relative speed included in the ADAS information, the estimation unit 113 has the stopped truck 301 at the predicted position (66.68 [m] ahead) of the moving body 100 at the prediction time. You can identify what to do. In this case, the estimation unit 113 determines that the target of the event is the track 301, and estimates this track 301 as the event occurrence location. As described above, the estimation unit 113 estimates an object (here, the track 301) that exists in the vicinity of the moving body 100 and exists at the arrival position after the required time as an event occurrence location.

  When the estimation process (S26) in the estimation unit 113 ends, the event prediction system 1 notifies the occurrence part of the event estimated by the estimation unit 113 in the notification unit 13 (S27). In the example of FIG. 4, the marker 401 (area indicated by dot hatching) is displayed around the track 301 estimated as the event occurrence location by the 3D-HUD 131. As a result, the driver is alerted to the truck 301 because the three-dimensional marker 401 appears to be displayed in an overlapping manner around the truck 301. That is, in the driver's field of view, augmented reality (AR) display in which the marker 401 displayed by the 3D-HUD 131 is synthesized in real space is realized.

  Accordingly, the driver can confirm that there is a “invisible danger” lurking like a pedestrian or a bicycle jumping out from behind the truck 301 that is a blind spot of the driver. Thus, according to the event prediction system 1 according to the present embodiment, it is possible to support the driving of the moving body 100 by the driver so as to realize safer driving.

  On the other hand, if the predicted required time is longer than the prescribed time S (S23: No), the event prediction system 1 skips steps S24 to S27 and ends the series of processes in the prediction block 11.

  The event prediction system 1 estimates the occurrence location of the event by repeatedly performing the processing of steps S21 to S27 at regular time intervals (for example, 0.1 seconds). Then, the event prediction system 1 estimates and notifies the occurrence location of the event every time the required time is equal to or less than the specified time S (S23: Yes).

  Further, the operation of the event prediction system 1 related to the estimation of the event occurrence location in the prediction block 11 is not limited to the example shown in FIG. For example, the order of the first acquisition process (S24) and the second acquisition process (S25) may be reversed.

(4) Supplementary items Several examples of events (dangers that cannot be seen) that can be predicted by the event prediction system 1 according to the present embodiment are given below. Here, it is assumed that the event prediction system 1 displays an occurrence location of an event in an image as seen from above including the moving body 100 (own vehicle).

  First, FIG. 5A, FIG. 5B, and FIG. 5C have shown the condition where a bicycle, a vehicle, a pedestrian, etc. can jump out from the shadow of a target object (here, a stopped track).

  In the example of FIG. 5A, each of the traveling lane 501A in which the host vehicle 300A that is the moving body 100 is traveling and the opposite lane 502A are one lane, and a plurality of parked trucks 301A are placed on the shoulders of the opposite lane 502A. , 302A, 303A. Further, from the sidewalk 503A on the opposite lane 502A side, the bicycle 304A is about to cross the travel lane 501A through between the track 302A and the track 303A. In the situation as shown in FIG. 5A, the event prediction system 1 “appears in the vicinity of a plurality of parked trucks 301A, 302A, 303A, for example, from information such as the inter-vehicle distances of the plurality of trucks 301A, 302A, 303A. Judge that there is no danger. Therefore, the event prediction system 1 displays the marker 401A in an area around the plurality of parked trucks 301A, 302A, and 303A. The situation illustrated in FIG. 5A is not limited to a case where a plurality of trucks 301A, 302A, and 303A are parked. For example, a plurality of trucks 301A, 302A, and 303A travel at a very low speed due to traffic congestion. It can also occur when you are.

  In the example of FIG. 5B, each of the traveling lane 501B and the opposite lane 502B in which the host vehicle 300B that is the moving body 100 is traveling has two lanes. Here, since the traffic light 504B has a red signal, there are a plurality of trucks 301B and 302B that are stopped (waiting for a signal) in front of the host vehicle 300B in the traveling lane 501B. Furthermore, there is a traveling track 303B in the oncoming lane 502B. In this case, from the parking space 505B on the sidewalk 503B on the side of the traveling lane 501B, the vehicle 304B is about to exit the opposite lane 502B through the space between the truck 301B and the truck 302B. In the situation as shown in FIG. 5B, for example, the event prediction system 1 has an “invisible danger” lurking around the stopped track 301B from information such as the inter-vehicle distances of the plurality of trucks 301B and 302B and the traffic light 504B. Judge that Therefore, the event prediction system 1 displays the marker 401B in the area around the stopped track 301B.

  In the example of FIG. 5C, each of the traveling lane 501C in which the vehicle 300C that is the moving body 100 is traveling and the opposite lane 502C are one lane, and a parked truck 301C exists on the shoulder of the traveling lane 501C. . In this case, the pedestrian 302C is crossing the pedestrian crossing 504C ahead of the track 301C toward the side 503C on the opposite lane 502C side. In the situation as shown in FIG. 5C, for example, the event prediction system 1 determines from the information such as the moving speed of the truck 301C and the pedestrian crossing 504C that “invisible danger” is lurking around the parked truck 301C. . Therefore, the event prediction system 1 displays the marker 401C in the area around the parked truck 301C.

  Moreover, FIG. 6A, FIG. 6B, and FIG. 6C have shown the condition where the vehicle exists in the blind spot produced by the target object (here truck).

  In the example of FIG. 6A, each of the traveling lane 501D in which the host vehicle 300D as the moving body 100 is traveling and the opposite lane 502D are one lane, and turn right from the left at the intersection in front of the host vehicle 300D. There is an incoming track 301D. Further, on the opposite lane 502D, there is a vehicle 302D waiting for a right turn in the same intersection. In the situation as shown in FIG. 6A, the event prediction system 1 determines that “invisible danger” is lurking in the blind spot generated in the truck 301D from information such as the truck 301D and the vehicle 302D, for example. Therefore, the event prediction system 1 displays the marker 401D in the blind spot area generated by the track 301D.

  In the example of FIG. 6B, each of the travel lane 501E in which the host vehicle 300E that is the moving body 100 is traveling and the opposite lane 502E are two lanes, and an intersection in front of the host vehicle 300E is at the travel lane 501E. There are a plurality of trucks 301E, 302E, 303E waiting for a right turn. Further, on the opposite lane 502E, there is a vehicle 304E waiting for a right turn at the intersection. In the situation as shown in FIG. 6B, for example, the event prediction system 1 uses the information such as the plurality of trucks 301E, 302E, 303E, and the vehicle 304E to detect the blind spots caused by the plurality of trucks 301E, 302E, 303E. "Is lurking. Therefore, the event prediction system 1 displays the marker 401E in a blind spot area generated by a plurality of tracks 301E, 302E, and 303E.

  In the example of FIG. 6C, each of the traveling lane 501F in which the host vehicle 300F that is the moving body 100 is traveling and the opposite lane 502F are two lanes, and the host vehicle 300F waits for a right turn at the intersection. Furthermore, there are a plurality of trucks 301F, 302F, 303F waiting for a right turn in the opposite lane 502F at the intersection. Furthermore, there is a vehicle 304F traveling straight in the oncoming lane 502F. In the situation as shown in FIG. 6C, for example, the event prediction system 1 determines from the information such as the top track 301F and the vehicle 304F that the “invisible danger” lurks in the blind spot generated by the top track 301F. Therefore, the event prediction system 1 displays the marker 401F in the blind spot area generated by the track 301F.

(5) Modifications Embodiment 1 is only one of various embodiments of the present invention. As long as the object of the present invention can be achieved, the first embodiment can be variously modified according to the design and the like. Moreover, the same function as the event prediction system 1 may be embodied by an event prediction method, a computer program, a storage medium storing the program, or the like. The event prediction method according to one aspect includes a first acquisition process, a second acquisition process, and an estimation process. The first acquisition process is a process of acquiring time information representing a required time until a predicted time point at which an event related to the driving of the moving body 100 is predicted to occur. The second acquisition process is a process for acquiring mobile object information representing the status of the mobile object 100. The estimation process is a process of estimating an event occurrence location based on time information and moving body information. A (computer) program according to one aspect is a program for causing a computer system to execute a first acquisition process, a second acquisition process, and an estimation process.

  Hereinafter, modifications of the first embodiment will be listed. The modifications described below can be applied in appropriate combinations.

  The estimation result of the event occurrence location is not limited to the configuration notified from the notification unit 13, and may be output to, for example, a vehicle control system that controls the moving body 100. In this case, the vehicle control system operates the brake, accelerator, steering, etc. according to the estimation result of the event occurrence location, thereby decelerating in advance before the event occurrence or avoiding the event occurrence location. be able to. As a result, automatic operation (including both fully automatic operation and partial automatic operation) can be realized in the vehicle control system.

  In addition, the event prediction system 1 only needs to have a function of estimating an occurrence location of an event based on time information indicating a required time until a prediction time point at which the occurrence of an event is predicted. This is not an essential function for the prediction system 1. Therefore, the event prediction system 1 may be configured to acquire the required time from another system, for example. That is, the time prediction unit 114 that predicts the required time using the prediction model, the storage unit 121 that accumulates learning data, the generation unit 122 that generates the prediction model, and the like are not essential components in the event prediction system 1. .

  Further, the configuration is not limited to the configuration in which the time prediction unit 114 transmits the label information to the learning block 12, and a part other than the time prediction unit 114 in the prediction block 11 may transmit the label information to the learning block 12. Furthermore, the time information as the label information may be added to the history information on the prediction block 11 side provided in the moving body 100. In this case, the learning block 12 accumulates the history information with label information received from the prediction block 11 in the accumulation unit 121 as needed. Further, the label information may be generated using the history information received from the prediction block 11 in the learning block 12 without being limited to the configuration in which the label information is transmitted from the prediction block 11 to the learning block 12. In this case, generation of label information and addition of label information to history information are both performed in the learning block 12.

  Further, the event prediction system 1 according to the first embodiment is not limited to being embodied by a system separated into the mobile object 100 and the cloud 200. For example, the event prediction system 1 may be housed in one housing, or may be integrated into the mobile object 100 or the cloud 200. For example, when the event prediction system 1 is integrated into the mobile object 100, the event prediction system 1 can also generate a prediction model in a stand-alone manner in the mobile object 100. In this case, for example, an EEPROM (Electrically Erasable and Programmable Read-Only Memory) and an ECU (Electronic Control Unit) incorporated in the moving body 100 function as an accumulation unit and a generation unit, respectively. Each component (estimation part 113, time prediction part 114, etc.) of event prediction system 1 may be distributed and provided in two or more devices. For example, the time prediction unit 114 may be provided in a distributed manner between the mobile object 100 and the cloud 200.

  Further, the learning block 12 is not limited to the configuration in which the history information serving as learning data is acquired from one moving body 100, but may be acquired (collected) from a plurality of moving bodies 100. In this case, the learning block 12 generates a prediction model using history information acquired from the plurality of mobile bodies 100 and transmits the generated prediction model to the plurality of mobile bodies 100. In particular, when the learning block 12 collects history information as learning data from a large number of mobile bodies 100, the collected history information set constitutes so-called big data.

  The learning block 12 may be installed in a store that performs sales and maintenance of automobiles, for example. In this case, the learning block 12 can acquire history information from a plurality of moving bodies 100 that receive maintenance at the store. The prediction model generated in the learning block 12 is transmitted to the prediction block 11 when the mobile object 100 is maintained. Thereby, in the mobile body 100, the prediction model can be updated at the time of maintenance. Furthermore, the learning block 12 may be embodied by a server device such as a sales company or a manufacturer that manages a plurality of stores, for example. In this case, the learning block 12 can centrally manage history information collected from a plurality of stores, and can generate a prediction model using these history information.

  Moreover, the information for prediction should just be the information showing the condition of the mobile body 100, and does not need to be the same information as the mobile body information which the 2nd acquisition part 112 acquires. For example, the moving speed of the moving body 100 included in the vehicle information in the prediction information may be substituted with the moving speed calculated from the position information in the moving body information. Similarly, the history information may be information indicating the status of the mobile object 100 and may not be the same as the mobile object information acquired by the second acquisition unit 112.

  In addition, the notification unit 13 is not limited to the configuration in which the 3D-HUD 131 performs the augmented reality display, and the 2D-HUD 132, the meter 133, the multi-information display 134, or the like may perform, for example, text display or animation display. Good. Moreover, the alerting | reporting part 13 may perform an augmented reality display by displaying the image | video which synthesize | combined the marker with the real-time image | video image | photographed with the front camera on the display etc. of a car navigation system. Furthermore, the alerting | reporting part 13 may have a display part which consists of wearable terminals, such as a head mounted display (HMD: Head Mounted Display).

  In addition, the notification unit 13 is not limited to the configuration for notifying by displaying the event occurrence location, but for example, is configured to notify the event occurrence location by voice, a haptic device, or a combination of these and display. Also good. Furthermore, the target of notification by the notification unit 13 is not limited to the driver of the moving body 100, for example, to the following vehicle of the moving body 100, pedestrians around the moving body 100, or the like by lighting of lamps or horns. On the other hand, the notification unit 13 may notify.

  Moreover, you may switch whether the estimation by the estimation part 113 and the alerting | reporting by the alerting | reporting part 13 are performed based on a driver | operator's state (a mental state is included) etc., for example. For example, when the driver is in a state where his / her concentration is reduced compared to the normal time due to fatigue, lack of sleep, etc., the driver may be less likely to notice the possibility of an event occurring. Therefore, for example, when the driver's driving is rougher than normal time based on the acceleration applied to the moving body 100 and the detection result of the driver monitor included in the vehicle information, the estimation unit 113 performs estimation. May be. Further, for example, when it is determined from the detection result of the driver monitor that the driver is not aware of the possibility of the occurrence of the event, the notification unit 13 may perform notification. Furthermore, you may change the level which alert | reports by the alerting | reporting part 13 based on a driver | operator's state.

  In addition, the estimation unit 113 is not limited to a configuration in which an object that exists in the vicinity of the moving body 100 and exists at the arrival position after the required time is estimated as an event occurrence location, but an event such as an intersection or a crosswalk occurs. You may estimate the place to perform as an event occurrence location.

  Further, the event predicted by the event prediction system 1 is not limited to an event (risk that cannot be seen) caused by an object that becomes a blind spot of the driver. The event prediction system 1 may predict the occurrence location of an event that can be predicted regardless of the state of the mobile object 100, such as a point where accidents frequently occur, the vicinity of the exit and entrance of the tunnel, and a sharp curve. .

  In addition, the event prediction system 1 is a so-called V2X (direct communication) between vehicles (between vehicles) or between vehicles and infrastructure such as traffic lights and road signs (between roads and vehicles). Vehicle to Everything) communication technology may be used. According to the V2X communication technology, for example, it is possible for the mobile unit 100 to acquire prediction information and the like used to predict the required time in the time prediction unit 114 from surrounding vehicles or infrastructure. Further, instead of the notification unit 13, it is possible to notify the occurrence location of the event by infrastructure. The location where an event occurs may be estimated in the infrastructure, and in this case, the event prediction system 1 may not be mounted on the mobile object 100.

  Moreover, the event prediction system 1 is applicable not only to a motor vehicle but to mobile bodies other than a motor vehicle, such as a two-wheeled vehicle, a train, an aircraft, a drone, a construction machine, and a ship. Furthermore, the event prediction system 1 is not limited to a mobile object, and may be used in an amusement facility, for example, as a wearable terminal such as a head mounted display (HMD), a medical facility, or a stationary device. May be used.

(Embodiment 2)
The event prediction system 1 according to the present embodiment is different from the event prediction system 1 according to the first embodiment in that the time prediction unit 114 uses a different prediction model for each attribute of the driver who drives the moving body 100. Hereinafter, the same configurations as those of the first embodiment are denoted by common reference numerals, and description thereof is omitted as appropriate.

  That is, in the first embodiment, the time prediction unit 114 predicts the required time using a prediction model common to all people, but in the present embodiment, the time prediction unit 114 is different for each driver attribute. The time required is predicted using a prediction model. The “driver attribute” here includes the age, gender, driving habit (such as how to step on the accelerator and brake), and the like of the driver.

  In the present embodiment, the learning block 12 acquires history information serving as learning data from a plurality of drivers. The generation unit 122 generates a prediction model for each driver attribute. As an example, the generation unit 122 applies a collaborative filtering algorithm used in a recommendation algorithm or the like and performs machine learning to generate a prediction model for each driver attribute.

  A prediction model to be applied (stored in the model storage unit 115) is selected for each moving object 100 from the plurality of types of prediction models generated in this way. That is, the prediction block 11 determines a prediction model to be acquired according to the attribute of the driver of the moving body 100. Thereby, the time prediction unit 114 can predict the required time using a different prediction model for each attribute of the driver.

  According to the event prediction system 1 according to the present embodiment, the prediction accuracy of the required time in the time prediction unit 114 is improved as compared with a case where a prediction model common to all people is used.

  In the event prediction system 1 according to the modification of the second embodiment, a plurality of prediction models are used properly in one mobile body 100. That is, when one moving body 100 is shared by a family, or in car sharing or the like, a plurality of drivers drive one moving body 100. According to this modification, in such a case, even with one mobile body 100, a different prediction model can be applied for each driver. Specifically, every time the driver changes, the prediction block 11 acquires a prediction model corresponding to the driver's attribute from the learning block 12. Or a some prediction model may be stored in the model storage part 115, and the prediction model which the time prediction part 114 uses according to a driver | operator's attribute from these several prediction models may be selected.

  The configuration (including the modification) of the event prediction system 1 according to the second embodiment can be appropriately combined with the configuration of the first embodiment (including the modification).

  The drawings shown in the above embodiments are merely conceptual diagrams for explaining an example of the event prediction system 1, and the shape, size, positional relationship, and the like of each part are appropriately different from the actual aspects.

(Summary)
As described above, the event prediction system (1) according to the first aspect includes the first acquisition unit (111), the second acquisition unit (112), and the estimation unit (113). A 1st acquisition part (111) acquires the time information showing the time required to the prediction time when generation | occurrence | production of the event relevant to a driving | running | working of a moving body (100) is estimated. A 2nd acquisition part (112) acquires the mobile body information showing the condition of a mobile body (100). The estimation unit (113) estimates the occurrence location of the event based on the time information and the moving body information.

  According to this configuration, the occurrence location of the event is estimated based on the required time until the predicted time when the occurrence of the event is predicted and the situation of the moving object (100). Therefore, in the event prediction system (1), there is an advantage that it is possible to predict the occurrence of an event (risk that cannot be seen) caused by an object that becomes a blind spot of the driver. Therefore, according to the event prediction system (1), it is possible to suppress the variation in predictability of “invisible danger” due to the driving skill of the driver, the driving sense, the driver's condition, and the like. As a result, for example, even a driver who has relatively little driving experience can perform driving considering the possibility of this type of event. Furthermore, for example, even when the driver is in a state of reduced concentration compared to normal times due to fatigue, lack of sleep, etc., the driver can drive considering the possibility of this type of event occurring. Become. In addition, an event may occur when the driver may be late in noticing the event, such as when the driver is just looking away or the driver is distracted. It becomes possible to notice the sex quickly, and safer driving becomes possible. In addition, since the event prediction system (1) estimates an event occurrence location using a one-dimensional parameter of a required time to the prediction time point, the processing load related to the estimation of the event occurrence location is kept relatively small. Can do.

  In the event prediction system (1) according to the second aspect, in the first aspect, the moving body information includes information on an object around the moving body (100), information on the state of the moving body (100), and movement. Information on the position of the body (100).

  According to this configuration, since the event prediction system (1) estimates an event occurrence location using at least one of ADAS information, vehicle information, and position information, the processing load related to the estimation of the event occurrence location is reduced. It can be kept relatively small.

  In the event prediction system (1) according to the third aspect, in the first or second aspect, the estimation unit (113) determines the arrival position of the mobile object (100) at the prediction time point based on the time information and the mobile object information. Is configured to predict. The estimation unit (113) is configured to estimate the occurrence location of the event from the arrival position.

  According to this configuration, since the occurrence location of the event is estimated from the arrival position of the mobile object (100) at the prediction time, the estimation accuracy of the occurrence location of the event is improved.

  In the event prediction system (1) according to the fourth aspect, in the third aspect, the estimation unit (113) exists in the vicinity of the moving object (100), and detects an object present at the arrival position after the required time. It is configured to estimate the occurrence location of the event.

  According to this configuration, since the object existing at the arrival position after the required time is estimated as the event occurrence location, it can be estimated as the event target even when the event target object is moving, for example. Become.

  The event prediction system (1) which concerns on a 5th aspect is further equipped with the alerting | reporting part (13) which alert | reports the generation | occurrence | production location of the event estimated in the estimation part (113) in any one of the aspects 1-4.

  According to this configuration, since the event occurrence location is notified, the driver or the like can drive while paying attention to the event occurrence location.

  In the event prediction system (1) according to the sixth aspect, in the fifth aspect, the notification unit (13) includes a display unit that notifies by displaying the occurrence location of the event.

  According to this configuration, since the event occurrence location is notified by display, the driver or the like can easily identify the event occurrence location.

  The event prediction system (1) which concerns on a 7th aspect is further equipped with the time estimation part (114) which estimates a required time in the aspect in any one of the 1st-6th. The time prediction unit (114) is configured to predict the required time using prediction information related to the moving object (100) and a prediction model.

  According to this configuration, since the required time is predicted by the event prediction system (1), it is not necessary to provide the event prediction system (1) with the required time from the outside, and processing for estimating the occurrence location of the event is performed. It can be completed only by the event prediction system (1).

  The event prediction system (1) according to the eighth aspect is configured such that, in the seventh aspect, the time prediction unit (114) uses a different prediction model for each attribute of the driver driving the mobile body (100). Has been.

  According to this configuration, the prediction accuracy of the required time in the time prediction unit (114) is improved as compared with the case where a prediction model common to all people is used.

  The event prediction system (1) according to the ninth aspect includes a storage unit (121) and a generation unit (122). The accumulating unit (121) accumulates a plurality of learning data including history information indicating the state of the moving body (100) when an event related to the driving of the moving body (100) occurs. The generation unit (122) generates a prediction model for predicting a required time to a prediction time point at which an occurrence of an event is predicted, using a plurality of learning data. Each of the plurality of learning data further includes label information indicating a time required until the event occurs.

  According to this configuration, a prediction model for predicting a required time until a prediction time point at which an occurrence of an event is predicted is generated. By using this prediction model, the event prediction system (1) has an advantage that it is possible to predict the occurrence of an event (risk that cannot be seen) due to an object that becomes a blind spot of the driver.

  The event prediction method according to the tenth aspect includes a first acquisition process, a second acquisition process, and an estimation process. A 1st acquisition process acquires the time information showing the required time to the prediction time when generation | occurrence | production of the event relevant to a driving | running | working of a moving body (100) is estimated. A 2nd acquisition process acquires the mobile body information showing the condition of a mobile body (100). In the estimation process, the occurrence location of the event is estimated based on the time information and the moving body information.

  According to this aspect, the occurrence location of the event is estimated based on the required time until the predicted time when the occurrence of the event is predicted and the situation of the moving object (100). Therefore, the event predicting method has an advantage that it is possible to predict the occurrence of an event (invisible danger) caused by an object that becomes a blind spot of the driver. Therefore, according to the event prediction method, it is possible to suppress the variation in the predictability of “invisible danger” due to the driving skill of the driver, the driving sense, the driver's condition, and the like. As a result, for example, even a driver who has relatively little driving experience can perform driving considering the possibility of this type of event. Furthermore, for example, even when the driver is in a state of reduced concentration compared to normal times due to fatigue, lack of sleep, etc., the driver can drive considering the possibility of this type of event occurring. Become. In addition, an event may occur when the driver may be late in noticing the event, such as when the driver is just looking away or the driver is distracted. It becomes possible to notice the sex quickly, and safer driving becomes possible. In addition, since the event prediction method estimates an event occurrence location using a one-dimensional parameter called a required time to the prediction time, the processing load related to the estimation of the event occurrence location can be kept relatively small.

  A program according to an eleventh aspect is a program for causing a computer system to execute a first acquisition process, a second acquisition process, and an estimation process. A 1st acquisition process acquires the time information showing the required time to the prediction time when generation | occurrence | production of the event relevant to a driving | running | working of a moving body (100) is estimated. A 2nd acquisition process acquires the mobile body information showing the condition of a mobile body (100). In the estimation process, the occurrence location of the event is estimated based on the time information and the moving body information.

  According to this aspect, the occurrence location of the event is estimated based on the required time until the predicted time when the occurrence of the event is predicted and the situation of the moving object (100). Therefore, this program has an advantage that it is possible to predict the occurrence of an event (invisible danger) caused by an object that becomes a blind spot of the driver. Therefore, according to this program, it becomes possible to suppress the variation in the predictability of “invisible danger” due to the driving skill of the driver, the driving sense, the driver's condition, and the like. As a result, for example, even a driver who has relatively little driving experience can perform driving considering the possibility of this type of event. Furthermore, for example, even when the driver is in a state of reduced concentration compared to normal times due to fatigue, lack of sleep, etc., the driver can drive considering the possibility of this type of event occurring. Become. In addition, an event may occur when the driver may be late in noticing the event, such as when the driver is just looking away or the driver is distracted. It becomes possible to notice the sex quickly, and safer driving becomes possible. In addition, since this program estimates the occurrence location of an event using a one-dimensional parameter that is the required time to the prediction time, the processing load related to the estimation of the occurrence location of the event can be kept relatively small.

  The mobile body (100) according to the twelfth aspect includes the event prediction system (1) according to any one of the first to ninth aspects.

  According to this configuration, there is an advantage that in the moving body (100), it is possible to predict the occurrence of an event (risk that cannot be seen) due to an object that becomes a blind spot of the driver.

  The various configurations (including modifications) of the event prediction system 1 according to the first embodiment and the second embodiment are not limited to the above aspects, and can be realized by an event prediction method and a (computer) program.

  About the structure which concerns on a 2nd-9th aspect, it is not a structure essential to an event prediction system (1), and can be abbreviate | omitted suitably.

DESCRIPTION OF SYMBOLS 1 Event prediction system 100 Mobile body 111 1st acquisition part 112 2nd acquisition part 113 Estimation part 114 Time prediction part 121 Accumulation part 122 Generation part 13 Notification part 131 3D-HUD (display part)
132 2D-HUD (display unit)
133 Meter (display unit)
134 Multi-information display (display unit)

Claims (12)

A first acquisition unit that acquires time information representing a required time until a predicted time point at which an event related to driving of the mobile object is predicted to occur;
A second acquisition unit for acquiring mobile body information representing the status of the mobile body;
An event prediction system comprising: an estimation unit that estimates an occurrence location of the event based on the time information and the moving body information. The event prediction system according to claim 1, wherein the moving object information includes at least one of information related to an object around the moving object, information related to a state of the moving object, and information related to a position of the moving object. . The estimation unit is configured to predict the arrival position of the moving object at the prediction time based on the time information and the moving object information, and to estimate the occurrence location of the event from the arrival position. Item 3. The event prediction system according to Item 1 or 2. The event according to claim 3, wherein the estimation unit is configured to estimate an object that exists in the vicinity of the moving body and exists at the arrival position after the required time as the occurrence location of the event. Prediction system. The event prediction system according to claim 1, further comprising a notification unit that notifies the occurrence location of the event estimated by the estimation unit. The event prediction system according to claim 5, wherein the notification unit includes a display unit that notifies by displaying the occurrence location of the event. A time prediction unit for predicting the required time;
The event prediction according to any one of claims 1 to 6, wherein the time prediction unit is configured to predict the required time using prediction information related to the moving object and a prediction model. system. The event prediction system according to claim 7, wherein the time prediction unit is configured to use the prediction model that is different for each attribute of a driver who drives the mobile body. An accumulator that accumulates a plurality of learning data including history information representing the status of the moving object when an event related to driving of the moving object occurs;
A generation unit that generates a prediction model for predicting a required time until a prediction time point at which the occurrence of the event is predicted, using the plurality of learning data;
Each of the plurality of learning data further includes label information indicating a required time until the event occurs. A first acquisition process for acquiring time information representing a required time until a predicted time point at which an event related to driving of the moving object is predicted to occur;
A second acquisition process for acquiring mobile object information representing the status of the mobile object;
An event prediction method comprising: an estimation process for estimating an occurrence location of the event based on the time information and the moving body information. Computer system,
A first acquisition process for acquiring time information representing a required time to a predicted time point at which an occurrence of an event related to driving of the moving object is predicted;
A second acquisition process for acquiring mobile object information representing the status of the mobile object;
Based on the time information and the mobile information, an estimation process for estimating the occurrence location of the event;
A program for running A moving body comprising the event prediction system according to claim 1.