Information processing device, information processing method, and information processing program

Abstract

This invention provides an information processing device, an information processing method, and an information processing program that provide information on locations where traffic is obstructed. [Solution] The information processing device comprises: an acquisition unit that acquires vehicle information generated by a vehicle; a first identification unit that identifies vehicle driving that falls under predetermined conditions different from normal vehicle driving based on the vehicle information acquired by the acquisition unit; a second identification unit that, when the first identification unit identifies vehicle driving that falls under predetermined conditions, identifies the location that falls under those predetermined conditions based on location information included in the vehicle information; and a providing unit that provides text content corresponding to the vehicle driving that falls under the predetermined conditions identified by the first identification unit and the location identified by the second identification unit as content.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] Conventionally, there is a device capable of detecting ponding on a road. When there is a rainfall record above a predetermined threshold value, it is determined as ponding by ponding determination based on CAN information, and when posting information on SNS representing a ponding report in the area where the ponding is determined is acquired, it is recorded as a ponding point. Thereby, a vehicle can select a route that does not pass through the ponding point (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The device described in Patent Document 1 records a point where there is an obstacle to passage using SNS posts. However, although it may be necessary to inform the public of a point where there is an obstacle to traffic such as a ponding point, Patent Document 1 does not disclose such notification to the public.

[0005] The present disclosure provides an information processing apparatus, an information processing method, and an information processing program that provide a point where there is an obstacle to traffic.

Means for Solving the Problems

[0006] One embodiment of the information processing device includes: an acquisition unit that acquires vehicle information generated by a vehicle; a first identification unit that identifies vehicle driving that falls under predetermined conditions different from normal vehicle driving based on the vehicle information acquired by the acquisition unit; a second identification unit that, when the first identification unit identifies vehicle driving that falls under predetermined conditions, identifies a location that falls under those predetermined conditions based on location information included in the vehicle information; and a providing unit that provides text content corresponding to the vehicle driving that falls under the predetermined conditions identified by the first identification unit and the location identified by the second identification unit as content. [Effects of the Invention]

[0007] The information processing device, information processing method, and information processing program disclosed herein can provide locations where traffic is obstructed. [Brief explanation of the drawing]

[0008] [Figure 1] This is a diagram (overview diagram) illustrating an information processing device according to one embodiment. [Figure 2] This is a block diagram illustrating an information processing device according to one embodiment. [Figure 3] This diagram illustrates an example of SNS post content. (A) shows an example of SNS post content indicating that an anomaly has occurred (first content), and (B) shows an example of SNS post content indicating that the anomaly has been resolved (second content). [Figure 4] This diagram illustrates an example of the association between content 1 and content 2. (A) shows a citation-style association, (B) shows a tree-style association, and (C) shows a link-style association (with a URL). [Figure 5] This is a flowchart illustrating an information processing method according to one embodiment. [Modes for carrying out the invention]

[0009] One embodiment will be described below.

[0010] [Overview of Information Processing Device 100] First, an overview of the information processing device 100 according to one embodiment will be described. Figure 1 is a diagram (overview diagram) illustrating an information processing device 100 according to one embodiment.

[0011] The information processing device 100 may be configured as a providing device (traffic information rapid reporting device) that provides users with information estimated using vehicle information (for example, the condition of road 300 and the driving status of vehicle 210) in near real-time. The information processing device 100 may be configured as a providing device that provides users with at least one of the contents of the condition of road 300 and the driving status of vehicle 210. The information processing device 100 may be configured as a providing device that provides the contents over a communication network such as an SNS (Social Networking Service). The information processing device 100 may be configured as a generating device that generates at least one of the contents of the condition of road 300 and the driving status of vehicle 210 (for example, SNS post content) to be provided to users. The information processing device 100 is not limited to the example device described above, but may be configured as various devices. The information processing device 100 may be a computer such as a server, desktop, laptop, tablet, or smartphone.

[0012] The information processing device 100 acquires vehicle information generated by the vehicle 210. Here, the vehicle 210 generates vehicle information, which includes the details of operations performed on the vehicle and information acquired by sensors mounted on the vehicle. The vehicle information may include various types of information acquired by the vehicle 210, such as location information, time information, steering angle information, acceleration information, driving speed information, outside temperature information, wiper operation status information, slip status information, and turn signal operation status information. The vehicle information may also include, for example, probe information, CAN information, and CAN probe information.

[0013] The information processing device 100 identifies vehicle 210 driving under predetermined conditions that differ from the normal driving of vehicle 210, based on vehicle information. The information processing device 100 may identify (determine) that vehicle 210 is driving under conditions different from the normal driving of vehicle 210 if any of the one or more pieces of information recorded in the vehicle information meet the predetermined conditions (i.e., the predetermined conditions are met). Normal driving of the vehicle 210 refers to, for example, smooth driving under normal (ordinary) conditions, and may also refer to driving of the vehicle when no abnormalities occur in the vehicle or on the road on which the vehicle is traveling. The driving of vehicle 210 that meets the specified conditions is different from the driving of vehicle 210 under normal conditions. The driving of vehicle 210 that meets the specified conditions may include, for example, sudden deceleration of vehicle 210, steering angle that cannot be detected under normal conditions (sudden steering), U-turns of vehicle 210, outside temperatures lower or higher than normal, faster wiper operation, low-speed driving, lane departure, and illumination of hazard lights and brake lights, etc., which indicate that the driving of vehicle 210 does not correspond to the driving of vehicle 210 under normal conditions.

[0014] As described above, when the information processing device 100 identifies the movement of a vehicle 210 that meets predetermined conditions, it identifies the location that meets those predetermined conditions based on the location information included in the vehicle information. As a specific example, when the information processing device 100 identifies at least one movement of the vehicle 210 (movement of the vehicle 210 that meets the predetermined conditions) from among sudden deceleration of the vehicle 210, steering angle that cannot be detected under normal conditions (sudden steering), U-turn of the vehicle 210, outside temperature lower or higher than normal, operation of wipers at a faster speed, driving at a low speed, and lane departure, based on the vehicle information, it identifies the location of the vehicle 210 that has come to meet those predetermined conditions (location of the vehicle 210 where the predetermined conditions occurred) (location of the abnormality) based on the same vehicle information (location information).

[0015] The information processing device 100 generates, for example, text content (abnormal warning) corresponding to the running of the vehicle 210 that meets the predetermined conditions as described above, and content (such as SNS posting content, etc.) including the position (abnormal occurrence point) specified as described above, and provides the SNS posting content to the user.

[0016] The information processing device 100, for example, estimates the situation of the road 300 on which the vehicle 210 travels and the running situation of the vehicle 210 from the running of the vehicle 210 that meets the predetermined conditions, and generates text content (abnormal warning) indicating the estimated situation of the road 300 and the running situation of the vehicle 210. As a specific example, when the running of the vehicle 210 that meets the predetermined conditions specified based on the vehicle information on the road 300 is "sudden deceleration of the vehicle" (refer to reference numeral 311 in FIG. 1) and "U-turn" (refer to reference numeral 312 in FIG. 1), the information processing device 100 estimates that the vehicle is in a situation where it "cannot pass through the road (road closed)", and generates text content (abnormal warning) such as "road closed".

[0017] Further, the information processing device 100 may generate, for example, text content (position text content) indicating the position (the position specified as described above) (abnormal occurrence point) (refer to reference numeral 321 in FIG. 1) of the vehicle 210 that meets the predetermined conditions specified based on the vehicle information (position information) by referring to map information such as a road map. The information processing device 100 may include in the position text content the time (text content indicating the time) that meets the predetermined conditions based on the vehicle information (time information). As an example, the information processing device 100 may specify the position of the vehicle 210 that meets the predetermined conditions in the road map (map information), and acquire text content such as the road name, intersection name, address name, and facility name at that position.

[0018] The information processing device 100 publicly discloses, for example, content including the text content (abnormal warning and position text content) generated as described above on a site on the communication network. In this case, the information processing device 100 may publicly disclose the content (SNS posting content) including the text content generated as described above to the user using the SNS.

[0019] [Details of Information Processing Apparatus 100] Next, the information processing apparatus 100 according to an embodiment will be described in detail. FIG. 2 is a block diagram for explaining the information processing apparatus 100 according to an embodiment.

[0020] The information processing apparatus 100 includes, for example, a communication unit 121, a storage unit 122, a display unit 123, a control unit 110, and the like. The communication unit 121, the storage unit 122, and the display unit 123 may be an embodiment of an output unit. The control unit 110 includes, for example, an acquisition unit 111, a first specifying unit 112, a second specifying unit 113, an estimation unit 114, a providing unit 115, and the like. The control unit 110 may be configured by, for example, an arithmetic processing unit of the information processing apparatus 100. The control unit 110 (for example, an arithmetic processing unit or the like) may implement the functions of each unit (for example, the acquisition unit 111, the first specifying unit 112, the second specifying unit 113, the estimation unit 114, the providing unit 115, and the like) by appropriately reading and executing various programs and the like stored in the storage unit 122 and the like. That is, the functions of each unit may be implemented by computer implementation.

[0021] The communication unit 121 is, for example, a communication interface capable of transmitting and receiving various information to and from a device (external device) outside the information processing apparatus 100.

[0022] The storage unit 122 may store various information and programs. An example of the storage unit 122 may be a memory, a solid state drive, a hard disk drive, and the like. Note that the storage unit ********** 122 may be, for example, a storage area and a server on the cloud.

[0023] The storage unit 122 stores map information related to a road map. The map information may record, for example, a road map, a road name, an intersection name, an address name, and a facility name. Further, the map information may be information on a road network represented by, for example, nodes indicating feature points of a road and links connecting adjacent nodes.

[0024] The display unit 123 is a display capable of displaying various characters, symbols, images, etc.

[0025] The acquisition unit 111 acquires vehicle information generated by the vehicle 210. The acquisition unit 111 may also acquire vehicle information from an external device, for example, via the communication unit 121. The external device here may be, for example, the vehicle 210 and the server 220 (see Figure 1).

[0026] Here, vehicle 210 generates vehicle information including the details of its operations and information acquired by sensors mounted on the vehicle. As a specific example, vehicle 210 acquires location information and time information using positioning systems such as GNSS and GPS. Similarly, as a specific example, vehicle 210 acquires information on the steering angle of the steering wheel (steering angle information), information on the acceleration applied to the vehicle (acceleration information), information on the vehicle's speed (driving speed information), and information on the vehicle's outside temperature (outside temperature information) using steering angle sensors, acceleration sensors, driving speed sensors, and outside temperature sensors mounted on the vehicle. Similarly, as a specific example, vehicle 210 acquires information on the on / off status of the wiper switch (wiper operation status) (wiper operation status information), information on the vehicle's slip status based on the operation status of the anti-lock braking system (ABS) (slip status information), and information on the on / off status of the turn signals, hazard lights, and brake lights (turn signal operation status) (turn signal operation status information). Vehicle 210 generates vehicle information that includes various information acquired by the vehicle, such as location information, time information, steering angle information, acceleration information, driving speed information, wiper operation status information, slip status information, and turn signal operation status information. Vehicle 210 may transmit the generated vehicle information to an external device (for example, an information processing device 100 or a server 220). Vehicle 210 may generate vehicle information at relatively short intervals and transmit that vehicle information to an external device at relatively short intervals. The relatively short intervals may be various time intervals such as 1 second, 10 seconds, 30 seconds, 1 minute, and 3 minutes, or they may be intervals of distance such as each time the vehicle 210 completes to travel through one section (e.g., one link) of a road map (e.g., a road network represented by nodes and links). Vehicle information may include, for example, probe information, CAN information, and CAN probe information.

[0027] Furthermore, the acquisition unit 111 may acquire event information, for example. The acquisition unit 111 may acquire event information from an external device, for example, via the communication unit 121. The external device here may be, for example, a server. The event information may be, for example, information recording the date and time of an event held by closing a road to traffic, and the section of the road that will be closed. Specific examples of such events may include marathon races and fireworks displays, among other events. As a specific example, the acquisition unit 111 may acquire event information from servers provided by information providers such as police stations, municipal offices, and event companies, or it may acquire event information by crawling websites that announce events published by such information providers.

[0028] The first identification unit 112 identifies vehicle 210 travel that falls under predetermined conditions different from normal vehicle travel, based on vehicle information acquired by the acquisition unit 111. In other words, the first identification unit 112 identifies vehicle 210 travel that falls under predetermined conditions that are different from normal vehicle travel and are presumed to indicate an abnormality on the road 300 or vehicle travel. The first identification unit 112 may, for example, identify (determine) that the vehicle 210 is driving in a manner different from that of a normal vehicle 210 if at least one of the one or more pieces of information recorded in the vehicle information satisfies a predetermined condition (if the predetermined condition is met). "Normal vehicle operation" refers to, for example, normal operation under typical circumstances. The driving of a vehicle 210 that meets the specified conditions is different from the driving of the vehicle 210 under normal (normal) conditions. Examples of driving of a vehicle 210 that meets the specified conditions include cases where the vehicle 210 drives differently from its normal (normal) conditions due to an abnormality in the road 300. Another example of driving of a vehicle 210 that meets the specified conditions is cases where the vehicle 210 drives differently from its normal (normal) smooth driving (driving in congestion) because the road 300 is congested. Yet another example of driving of a vehicle 210 that meets the specified conditions is cases where the driving environment of the vehicle 210 (e.g., weather and outside temperature) is relatively bad, causing the vehicle 210 to drive differently from its normal (normal) conditions. The predetermined conditions may include, for example, sudden deceleration of the vehicle 210 (a relatively large acceleration value), a steering angle that cannot be detected under normal conditions (sudden steering), a U-turn by the vehicle 210, an outside temperature lower or higher than normal, faster wiper operation, a relatively low driving speed (for example, alternating between stopping and driving at a relatively slow speed), lane departure (lane departure estimated based on position information), and other conditions that indicate that the vehicle 210 is not driving under normal conditions. As an example, a threshold value for meeting the predetermined conditions may be predetermined, or there may be no threshold value for meeting the predetermined conditions predetermined.

[0029] Furthermore, the first identification unit 112 identifies vehicle 210 movements that meet predetermined conditions that differ from (or may differ from) normal vehicle 210 movements, based on event information acquired by the acquisition unit 111. The driving of a vehicle 210 that meets the specified conditions is different from the driving of a vehicle 210 under normal circumstances (normal times). In other words, the driving of a vehicle 210 that meets the specified conditions may be, for example, driving of a vehicle 210 that is unable to pass through a road closure section, etc., due to an event recorded in the event information (and is making a U-turn or detouring), or driving of a vehicle 210 that is congested on a road 300 relatively close to a road closure section. The specified conditions may include, for example, conditions that indicate that the vehicle 210 does not operate under normal conditions, such as making a U-turn, taking a detour, or driving at a relatively low speed (for example, repeatedly stopping and driving at a relatively slow speed).

[0030] As described above, the first identification unit 112 identifies vehicle 210 travel that falls under predetermined conditions different from the normal travel of vehicle 210, based on the vehicle information and event information acquired by the acquisition unit 111. In this disclosure, as an example, the concept of "vehicle information" may include "event information."

[0031] When the first identification unit 112 identifies the movement of a vehicle 210 that meets predetermined conditions, the second identification unit 113 identifies the location (location of the abnormality) that meets those predetermined conditions based on the location information included in the vehicle information. As a specific example, when the second identification unit 113 identifies at least one movement of the vehicle 210 (movement of the vehicle 210 that meets the predetermined conditions) from among sudden deceleration of the vehicle 210, steering angle that cannot be detected under normal conditions (sudden steering), U-turn of the vehicle 210, outside temperature lower or higher than normal, faster wiper operation, low-speed driving, lane departure, etc., based on the vehicle information (location information), the second identification unit 113 identifies the location of the vehicle 210 that has come to meet those predetermined conditions (location of the vehicle 210 where the predetermined conditions occurred).

[0032] The estimation unit 114 identifies the movement of a vehicle 210 that meets predetermined conditions using the first identification unit 112 described above, and then identifies the location (abnormality location) that meets those predetermined conditions using the second identification unit 113. Based on the vehicle information further acquired by the acquisition unit 111, the estimation unit 114 estimates whether the vehicle 210 moved at the location (abnormality location) that meets those predetermined conditions, that is, whether the vehicle 210 began moving in a manner that does not meet the predetermined conditions. In other words, the estimation unit 114 estimates the movement of the vehicle 210 at the location identified by the second identification unit 113 (resumption of normal vehicle 210 movement) based on the vehicle information acquired by the acquisition unit 111. In other words, the estimation unit 114, for example, when a vehicle 210 drives in a manner different from its normal (normal) driving, identifies the driving of the vehicle 210 that meets the predetermined conditions in the first identification unit 112 described above, further identifies the location (location of the abnormality) that meets those predetermined conditions in the second identification unit 113, and after the provision unit 115 provides the text content (abnormality warning) and location (location of the abnormality), the acquisition unit 111 further acquires vehicle information (vehicle information generated by one or more vehicles 210 (not limited to the vehicle that drove in a manner different from its normal (normal) driving) to estimate whether the driving of the vehicle 210 that meets the predetermined conditions in the first identification unit 112 is no longer identified at the location (location of the abnormality) identified by the second identification unit 113, and whether the vehicle 210 is now driving normally.

[0033] Figure 3 is a diagram illustrating an example of SNS post content. Figure 3(A) shows an example of a social media post (first content) indicating that an anomaly has occurred, and Figure 3(B) shows an example of a social media post (second content) indicating that the anomaly has ended.

[0034] The providing unit 115 provides text content (abnormal warning) corresponding to the driving of a vehicle 210 that meets predetermined conditions identified by the first identifying unit 112, and the location identified by the second identifying unit 113 (see Figure 3(A)). In other words, the providing unit 115 provides text content (abnormal warning) indicating that an abnormality has occurred on the road 300, corresponding to the driving of a vehicle 210 that meets predetermined conditions identified by the first identifying unit 112, and the location where the abnormality occurred, identified by the second identifying unit 113, over the communication network. For example, the providing unit 115 generates text content corresponding to the driving of a vehicle 210 that meets the predetermined conditions specified above, and content including the location specified above (for example, SNS post content), and provides the SNS post content to the user. That is, for example, the providing unit 115 may publish content including text content and location (SNS post content) on an SNS provided by a company equipped with the information processing device 100. The user can access the SNS (via the application providing the SNS) using a terminal such as a smartphone or tablet (not shown) and view the SNS post content.

[0035] The provisioning unit 115 estimates, for example, the condition of the road 300 on which the vehicle 210 is traveling and the driving conditions of the vehicle 210 based on the driving of the vehicle 210 that meets predetermined conditions, and generates text content (abnormal warning) indicating the estimated condition of the road 300 and the driving conditions of the vehicle 210.

[0036] As a more specific example, based on vehicle information and map information, the provisioning unit 115 may, when vehicles 210 in opposing lanes (both lanes) of the road 300 are making a "U-turn" as a vehicle 210 that meets predetermined conditions identified by the first identification unit 112, generate text content (abnormal warning) corresponding to the movement of the vehicle 210 that meets the predetermined conditions, such as "Road Closed".

[0037] As a more specific example, based on vehicle information and map information, if the vehicle 210 that meets predetermined conditions identified by the first identification unit 112 is experiencing "strong wiper operation" and "vehicle speed is slower than normal (reduced speed)", the provision unit 115 may generate text content (abnormal warning) corresponding to the vehicle 210's movement that meets the predetermined conditions, such as "Caution: Visibility is poor due to heavy rain."

[0038] As a more specific example, based on vehicle information and map information, if the vehicle 210 that meets predetermined conditions identified by the first identification unit 112 is "driving in a mountainous area" and vehicles 210 in opposing lanes (both lanes) of the road 300 are "making a U-turn", the provision unit 115 may generate text content (abnormal warning) corresponding to the driving of the vehicle 210 that meets the predetermined conditions, such as "possibility of landslide".

[0039] As a more specific example, based on vehicle information and map information, if the vehicle 210 meets predetermined conditions identified by the first identification unit 112, and the vehicle's wipers are operating at full speed, the vehicle is traveling near a river, and vehicles 210 in opposing lanes (both lanes) of the road 300 are making a U-turn, the provision unit 115 may generate text content (abnormal warning) corresponding to the vehicle 210 meeting the predetermined conditions, such as "possibility of flooding."

[0040] As a more specific example, based on vehicle information and map information, if the vehicle 210 meets predetermined conditions identified by the first identification unit 112, and if the vehicle 210 is "often deviating from its lane based on vehicle information (location information) (increased GPS position deviation)" and "the vehicle is traveling in a mountainous area", the provision unit 115 may generate text content (abnormal warning) corresponding to the vehicle 210's travel that meets the predetermined conditions, such as "possibility of road deformation due to landslide".

[0041] As a more specific example, the providing unit 115 may, based on vehicle information and map information, generate text content (abnormal warning) corresponding to the driving of a vehicle 210 that meets predetermined conditions identified by the first identifying unit 112, such as "the speed of the convoy (multiple vehicles) is gradually decreasing" and "many brake lights are illuminated." In this case, the congestion may be, for example, congestion of various distances, such as 3 km or more, 5 km or more, and 10 km or more.

[0042] As a more specific example, based on vehicle information and map information, if the vehicle 210 that meets predetermined conditions identified by the first identification unit 112 has "multiple vehicles stopped in the direction of travel of the vehicle" and "all of those vehicles have their hazard lights on", the provision unit 115 may generate text content (abnormal warning) corresponding to the vehicle 210 that meets the predetermined conditions, such as "possibility of a traffic accident".

[0043] As a more specific example, the provisioning unit 115 may, based on vehicle information and map information, generate text content (abnormal warning) corresponding to the driving of a vehicle 210 that meets predetermined conditions identified by the first identification unit 112, such as "slower driving speed during weekday commuting hours (abnormally low speed)," which may include "possibility of traffic congestion, possibility of traffic disruption due to an accident."

[0044] As a more specific example, based on vehicle information and map information, if the vehicle 210, as identified by the first identification unit 112, is driving under "sub-zero temperatures," has had "emergency braking" performed, and is "skidding (anti-lock braking system (ABS) (skidding sensor) activated)," the provision unit 115 may generate text content (abnormal warning) corresponding to the driving of the vehicle 210 that meets the predetermined conditions, such as "possibility of road freezing."

[0045] As a more specific example, the providing unit 115 may, based on vehicle information, event information, and map information, generate text content (anomaly warning) corresponding to the movement of a vehicle 210 that meets the predetermined conditions, such as "traffic congestion due to an event," when "the speed of a convoy (multiple vehicles) is gradually decreasing" and "many brake lights are illuminated" (anomaly occurrence), and an event is being held near the location of the anomaly occurrence (anomaly location).

[0046] In this case, the providing unit 115 may, for example, use a trained model generated by learning the driving of a vehicle 210 that meets predetermined conditions, the conditions of the road 300 and the driving conditions of the vehicle 210 estimated from the driving of the vehicle 210, and text content (abnormal warning) associated with the conditions of the road 300 and the driving conditions of the vehicle 210, to generate text content (abnormal warning) corresponding to the driving of a vehicle 210 that meets predetermined conditions as described above. In this case, the providing unit 115 may use a generation AI or the like. Alternatively, the providing unit 115 may, for example, pre-generate correspondence information (correspondence information) that records multiple patterns of the driving of a vehicle 210 that meets predetermined conditions, the conditions of the road 300 and the driving conditions of the vehicle 210 estimated from the driving of the vehicle 210, and text content (abnormal warning) associated with the conditions of the road 300 and the driving conditions of the vehicle 210, and then refer to this correspondence information to acquire (generate) text content (abnormal warning) corresponding to the driving of a vehicle 210 that meets the predetermined conditions specified as described above.

[0047] Furthermore, the providing unit 115 may, for example, refer to map information such as a road map to generate text content or a map image indicating the location of a vehicle 210 that meets predetermined conditions identified based on vehicle information (location information) (the location identified as described above). In other words, the providing unit 115 may use location information to obtain at least one name from among the road name, intersection name, address name, and facility name of the location (abnormal occurrence point) identified by the second identification unit 113 from the map information stored in the storage unit 122, and provide the obtained name as text content. For example, the providing unit 115 identifies the location of a vehicle 210 that meets predetermined conditions on a road map (map information) and obtains text content (location text content) such as the road name, intersection name, address name, and facility name at that location. As a specific example, the road name may be "National Route 246 Aoyama-dori," etc. Similarly, as a specific example, the intersection name may be "Miyakezaka," etc. Similarly, as a specific example, the address name may be "Nagatacho," etc. The facility name may be the name of a facility relatively close to the location of the vehicle 210 that meets predetermined conditions, and as a specific example, it may be "library," etc. Furthermore, the supply unit 115 may use a road map (map information) to generate a map image that records the location of a vehicle 210 that meets predetermined conditions. The provisioning unit 115 publishes, for example, the content including the generated text content (anomaly warning) and location text content (map image) as described above to users on a site on the communication network (e.g., SNS and websites).

[0048] As a result, the providing unit 115 acquires vehicle information transmitted from the vehicle 210 at relatively short intervals, as described above, and is able to provide the user with text content (abnormal warning) and location (at least one of location text content and map image) in real time (as soon as the vehicle 210 that meets the predetermined conditions is driven).

[0049] The providing unit 115 may provide text content and location as content if the current location of the vehicle 210, acquired based on location information, and the location (abnormality location) identified by the second identification unit 113 are separated by a predetermined distance or more. That is, when the information processing device 100 identifies a location (abnormality location) that meets predetermined conditions using the second identification unit 113 based on vehicle information (first vehicle information) generated by the vehicle 210, as described above, the acquiring unit 111 may continue to acquire vehicle information (second vehicle information) generated by the vehicle 210. The providing unit 115 acquires the current location of the vehicle 210 (the latest location of the vehicle 210) based on location information recorded in the first vehicle information or the second vehicle information. The providing unit 115 determines whether the distance between the current location of the vehicle 210 and the location (abnormality location) that meets predetermined conditions identified by the second identification unit 113 based on the first vehicle information generated by the vehicle 210 is separated by a predetermined distance or more. The predetermined distance may be a distance that can protect the privacy of the vehicle 210's users (the driver and passengers of the vehicle 210), that is, a distance at which the users of the vehicle 210 are expected to be relatively far from the location where the anomaly occurred. As a specific example, the predetermined distance may be various distances, including 1 km, 3 km, and 10 km. If the providing unit 115 determines that the distance between the current location of the vehicle 210 and the location that meets the predetermined conditions (location where the anomaly occurred) is greater than or equal to the predetermined distance, it may publish the above-mentioned content (SNS post content) on the communication network (SNS).

[0050] The provisioning unit 115 may, with respect to anomaly occurrence locations within the same (or nearly identical) area (same area), not provide the user with duplicate content including text content and location (SNS post content), but may provide the user with new SNS post content in association with the preceding SNS post content.

[0051] In this case, when the providing unit 115 uses location information and map information as described above to obtain at least one name from among the road name, intersection name, address name, and facility name of the location (location where an anomaly occurred) identified by the second identifying unit 113, the road section between the intersections from which the name was obtained, a bridge with the same name, an elevated structure with the same name, a tunnel with the same name, a road in a section of a certain length, etc., may be considered the same area as described above. As a specific example, when providing SNS posts in the case where there is one abnormality location in the Roppongi Tunnel on Tokyo Metropolitan Road 319 Loop Line 3, the providing unit 115 may, instead of providing two separate SNS posts about the abnormality on Tokyo Metropolitan Road 319 Loop Line 3 and the abnormality in the Roppongi Tunnel, provide an SNS post stating that an abnormality occurred in the Roppongi Tunnel in association with the SNS post stating that an abnormality occurred on Tokyo Metropolitan Road 319 Loop Line 3.

[0052] For example, when associating multiple SNS posts, the providing unit 115 may determine the higher-ranking SNS posts according to a pre-set priority, and then associate lower-ranking SNS posts with those higher-ranking posts. For example, the providing unit 115 may prioritize roads with higher standards, such as Road 300, and lower-ranking roads with lower standards.

[0053] Furthermore, when the providing unit 115 associates a subsequent SNS post with a preceding SNS post, it may count up the number of positive ratings (e.g., the number of "likes") and the number of views of the preceding SNS post. In this way, it is also possible to say that the providing unit 115 has associated a subsequent SNS post with a preceding SNS post. Furthermore, when the providing unit 115 associates subsequent SNS posts with preceding SNS posts, it may identify the SNS post with the highest number of positive ratings (e.g., the number of "likes") and the number of views of the preceding SNS post, and associate the subsequent SNS post with the identified SNS post as the preceding SNS post.

[0054] Figure 4 is a diagram illustrating an example of the association between the first content and the second content. Figure 4(A) shows a citation-style association, Figure 4(B) shows a tree-style association, and Figure 4(C) shows a link-style association (with URLs).

[0055] The providing unit 115 may, for example, associate a subsequent SNS post content (second content) with a preceding SNS post content (first content) by quoting the first content to provide the second content (see Figure 4(A)), provide the second content in a tree structure with respect to the first content (see Figure 4(B)), or provide the second content by including a link to access the first content (see Figure 4(C)). As a specific example, the providing unit 115 may provide the second content by performing a "self-reply" that replies to the first content.

[0056] When the provisioning unit 115 estimates the movement of the vehicle 210 at a location using the estimation unit 114, it may also provide information indicating that the situation described in the information has ended, in relation to information provided at a time earlier than the estimation (see Figure 3(B)). That is, as described above, the provisioning unit 115 identifies the movement of the vehicle 210 that meets predetermined conditions using the first identification unit 112, and further identifies the location (location of the anomaly) that meets those predetermined conditions using the second identification unit 113. In response to this, the provisioning unit 115 provides the user with the information including the text content and location text content (map image) described above (first content) (SNS post content), and then when the estimation unit 114 estimates the movement of the vehicle 210 at that location (location of the anomaly) (that the vehicle 210 has returned to normal movement), it may provide the user with information indicating that the movement of the vehicle 210 that meets the predetermined conditions has ended (that the vehicle 210 has returned to normal movement) (second content) (SNS post content) on the communication network (SNS), in relation to the first content (SNS post content). The providing unit 115 may, for example, provide the second content in relation to the first content by referencing the first content (see Figure 4(A)), by providing the second content in a tree structure relative to the first content (see Figure 4(B)), or by including a link to access the first content in the second content (see Figure 4(C)). As a specific example, the providing unit 115 may provide the second content by performing a "self-reply" that replies to the first content.

[0057] As a specific example, if the service provider 115 determines, based on the event information, that the event recorded in that event information has ended, it may provide a second SNS post (second content) that includes text such as "event ended" in association with the first SNS post (first content) that includes an abnormal warning related to the occurrence of the event (for example, text such as "traffic congestion due to the event").

[0058] The provisioning unit 115 may also control the output unit to output the content provided to the user (SNS post content) and the history of that provision (log). The output unit may be, for example, a communication unit 121, a storage unit 122, and a display unit 123.

[0059] [Information Processing Methods] Next, an information processing method according to one embodiment will be described. Figure 5 is a flowchart illustrating an information processing method according to one embodiment.

[0060] In step ST101, the acquisition unit 111 acquires vehicle information generated by the vehicle 210.

[0061] In step ST102, the first identification unit 112 identifies the driving of the vehicle 210 (special vehicle driving) that falls under predetermined conditions different from the normal driving of the vehicle 210, based on the vehicle information acquired in step ST101. In other words, the first identification unit 112 identifies the vehicle 210's movement (special vehicle movement) that is different from the normal movement of the vehicle 210 and falls under predetermined conditions that indicate an abnormality on the road 300 or movement of the vehicle 210 has occurred.

[0062] In step ST103, if the second identification unit 113 identifies the movement of a vehicle 210 that meets predetermined conditions (special vehicle movement) in step ST102, it identifies the location that meets those predetermined conditions (location of abnormality) based on the location information included in the vehicle information acquired in step ST101.

[0063] In step ST104, the providing unit 115 provides content (first content) (for example, SNS post content) over the communication network, which includes text content (abnormal warning) corresponding to the driving of the vehicle 210 that meets the predetermined conditions identified in step ST102 (special vehicle driving), and content corresponding to the location (location where the abnormality occurred) identified in step ST103. The providing unit 115 may use the vehicle information (location information) acquired in step ST101 to acquire at least one name from among the road name, intersection name, address name, and facility name of the location (location where the anomaly occurred) identified in step ST103, from the map information stored in the storage unit 122. In this case, the providing unit 115 may include the acquired name in the text content (anomaly warning), that is, it may provide content (for example, SNS post content) that includes the acquired name and the text content (anomaly warning). If the current location of the vehicle 210, which is obtained based on the vehicle information (location information) obtained in step ST101, and the location (location of the anomaly) identified in step ST103 are separated by a predetermined distance or more, the providing unit 115 may provide content (for example, SNS post content) that includes text content and location (location of the anomaly).

[0064] In step ST105, the acquisition unit 111 acquires vehicle information generated by the vehicle 210.

[0065] In step ST106, the estimation unit 114 estimates the movement of vehicle 210 at the location (abnormality location) identified in step ST103 based on the vehicle information acquired in step ST105 (determining whether the normal movement of vehicle 210 has resumed (or whether the special vehicle movement has ended)). If the providing unit 115 estimates the movement of the vehicle 210 at a given location using the estimation unit 114 (determining that the vehicle 210 has resumed normal movement (that the special vehicle movement has ended)), it may provide content (second content) (for example, SNS post content) indicating that the situation described in the content (first content) provided at a time earlier stage (step ST104) has ended, in relation to the content (first content) provided at that time earlier.

[0066] [Regarding functions and circuitry] Next, the functions and circuitry of the information processing device 100 described above will be explained. Each part of the information processing device 100 may be implemented as a function of a computer's arithmetic processing unit or the like. The information processing device 100 may, for example, implement the functions of the acquisition unit 111, the first identification unit 112, the second identification unit 113, the estimation unit 114, and the provision unit 115 with a single control unit 110 (e.g., an arithmetic processing unit, etc.), or it may implement the functions of the acquisition unit 111, the first identification unit 112, the second identification unit 113, the estimation unit 114, and the provision unit 115 in a distributed manner with multiple different control units 110 (e.g., arithmetic processing units, etc.). The acquisition unit 111, first identification unit 112, second identification unit 113, estimation unit 114, and provision unit 115 (control unit 110) of the information processing device 100 described above may be implemented as an acquisition function, first identification function, second identification function, estimation function, and provision function (control function), respectively, by a computer's arithmetic processing unit or the like. The information processing program can enable a computer to implement each of the functions described above. The information processing program may be recorded on a computer-readable, non-temporary, tangible recording medium such as memory, a solid-state drive, a hard disk drive, or an optical disc. The storage medium may be rephrased as, for example, a non-temporary, tangible, computer-readable medium for storing the information processing program. The information processing program may also be transmitted online. The information processing program can be implemented into a product (computer program product) by the control unit 110 (for example, an arithmetic processing unit). Furthermore, as described above, each part of the information processing device 100 may be implemented as a computer's arithmetic processing unit or the like. This arithmetic processing unit or the like is composed of, for example, an integrated circuit. For this reason, each part of the information processing device 100 may be implemented as a circuit that constitutes the arithmetic processing unit or the like. That is, the acquisition unit 111, the first identification unit 112, the second identification unit 113, the estimation unit 114, and the providing unit 115 (control unit 110) of the information processing device 100 may be implemented as an acquisition circuit, a first identification circuit, a second identification circuit, an estimation circuit, and a providing circuit (control circuit) that constitute the computer's arithmetic processing unit or the like. Furthermore, the communication unit 121, storage unit 122, and display unit 123 (output unit) of the information processing device 100 may be implemented as a communication function, storage function, and display function (output function) that includes the functions of an arithmetic processing unit, for example. Also, the communication unit 121, storage unit 122, and display unit 123 (output unit) of the information processing device 100 may be implemented as a communication circuit, storage circuit, and display circuit (output circuit) by being composed of an integrated circuit, for example. Furthermore, the communication unit 121, storage unit 122, and display unit 123 (output unit) of the information processing device 100 may be configured as a communication device, storage device, and display device (output device) by being composed of a plurality of devices, for example.

[0067] The information processing device 100 can be configured to combine one or any multiple of the above-described parts. In this disclosure, the term "information" is used, but the term "information" can be replaced with "data," and the term "data" can be replaced with "information."

[0068] [Aspects and Effects of This Embodiment] Next, an embodiment of this model and the effects of each embodiment will be described. Note that the embodiments described below are examples as of the time of filing, and this embodiment is not limited to the embodiments described below. In other words, this embodiment is not limited to the embodiments described below, and may be realized by appropriately combining the parts described above. Furthermore, lower-level embodiments may be referenced in any of the higher-level embodiments. Furthermore, the effects of this embodiment described below are merely examples, and the effects achieved by each embodiment are not limited to those described below. Also, each embodiment may achieve, for example, at least one of the effects described below.

[0069] (Aspect 1) One embodiment of the information processing device includes: an acquisition unit that acquires vehicle information generated by a vehicle; a first identification unit that identifies vehicle driving that falls under predetermined conditions different from normal vehicle driving based on the vehicle information acquired by the acquisition unit; a second identification unit that, when the first identification unit identifies vehicle driving that falls under predetermined conditions, identifies a location that falls under those predetermined conditions based on location information included in the vehicle information; and a providing unit that provides text content corresponding to the vehicle driving that falls under the predetermined conditions identified by the first identification unit and the location identified by the second identification unit as content. As a result, the information processing device acquires vehicle information transmitted from the vehicle at relatively short intervals, as described above, and can provide the user with text content (and image information) in real time (as soon as a vehicle meeting the predetermined conditions is driven), prompting the user to drive while avoiding the location (abnormality location) that meets the predetermined conditions. Furthermore, by utilizing vehicle information (e.g., CAN probe information), the information processing device can ensure comprehensive coverage of roads to identify the location of the anomaly.

[0070] Incidentally, when vehicle users go out in bad weather, they sometimes check real-time road conditions and public transport status on social media and adjust their departure time and travel route accordingly. However, while road administrators' SNS posts were generally reliable, the content of these posts was limited to roads under their jurisdiction, resulting in a lack of comprehensive information. Furthermore, while general users' SNS posts automatically recorded location information, allowing for relatively accurate identification of accident and flood-prone areas on public roads, some users had the location information feature turned off. Additionally, to protect their privacy, users might post after moving some distance from a traffic-prone area (resulting in discrepancies between the traffic-prone location and the location information provided in the SNS post), potentially undermining the reliability of these posts. In short, information sources closely tied to the daily lives of general users were unreliable. Moreover, it was rare for general users to post about the resolution of traffic-prone conditions. Additionally, general users' SNS posts often used diverse language, making it difficult for other users to find desired information unless it could be searched using specific keywords and hashtags. Finally, the privacy of those making SNS posts must be protected.

[0071] Therefore, as a specific example, the information processing device of this embodiment uses CAN probe information (vehicle information) to detect locations where abnormalities occur that meet predetermined conditions different from normal vehicle driving, and posts to social media, thereby informing users of the locations where abnormalities have occurred. Furthermore, as a specific example, the information processing device of this embodiment incorporates road names, intersection names (both ends of road links), address names, and facility names from map information into the aforementioned SNS posts using CAN probe information (vehicle information) (location information) (see Embodiment 3 described later). This makes it possible to clearly inform users of the location of the anomaly and improves user convenience. Furthermore, as a specific example, the information processing device of this embodiment, using CAN probe information (vehicle information), detects a driving history that negates the predetermined conditions described above, and in association with the aforementioned SNS post made when the location of the abnormality was detected, sends a reply (SNS post) indicating that the abnormality has ended (see Embodiment 2 described later). This allows the user to be notified that the abnormality has ended, thereby improving user convenience.

[0072] Furthermore, the information processing device of this embodiment is An acquisition unit that acquires multiple pieces of vehicle information generated by the vehicle, A first identification unit identifies the driving of a vehicle that meets predetermined conditions different from normal vehicle driving, based on multiple vehicle information acquired by the acquisition unit. When the first identification unit identifies the movement of a vehicle that meets predetermined conditions, the second identification unit identifies one location (one abnormality location) that meets those predetermined conditions based on the location information contained in each of the multiple vehicle information items, A providing unit that provides text content corresponding to the driving of a vehicle that meets predetermined conditions identified by the first identifying unit, and a single location (a single location where an abnormality occurred) identified by the second identifying unit, It may be provided. This allows the information processing device to identify a single location where an anomaly occurred based on information from multiple vehicles, thereby increasing the accuracy of the identified location (the location of the anomaly) and improving the reliability of the information provided to the user.

[0073] (Aspect 2) An information processing device in one embodiment includes an estimation unit that estimates the movement of a vehicle at a location specified by a second identification unit based on vehicle information acquired by an acquisition unit, and when the estimation unit estimates the movement of a vehicle at a location, the providing unit may also provide information indicating that the status of the content has been completed, in relation to content provided at a time earlier than the estimation. This allows the information processing device to inform the user that the vehicle has completed its operation under predetermined conditions different from normal vehicle operation (an abnormality at the location where the abnormality occurred), thereby improving user convenience. In other words, the information processing device can avoid duplicate SNS posts for anomaly occurrence locations within the same (or nearly identical) area, and can make new SNS posts (reflecting (posting) the latest information) in association with preceding SNS posts. The information processing device can avoid the risk of information being buried due to a relatively large number of SNS posts for anomaly occurrence locations within the same (or nearly identical) area, and can control the information.

[0074] Here, the information processing device includes an estimation unit that estimates the movement of a vehicle at a location (location of an anomaly) identified by a second identification unit based on at least one vehicle information acquired by an acquisition unit, and the providing unit provides text content corresponding to the movement of a vehicle that meets predetermined conditions identified by a first identification unit, and the location (location of an anomaly) identified by the second identification unit as content (first content). If the estimation unit estimates the movement of one (at least one) vehicle at that location (location of the anomaly), it may provide content (second content) indicating that the situation of the content (first content) has ended, in relation to the content (first content) provided at a time earlier than the estimation. As a result, if the information processing device can estimate (confirm) that even one vehicle was traveling at a previously identified anomaly location, it can estimate that the anomaly at that location has ended. Therefore, it can more quickly provide the user with the second piece of information indicating that the situation described in the first piece of information has ended (the anomaly has ended).

[0075] (Aspect 3) One embodiment of the information processing device includes a storage unit that stores map information relating to a road map, and a providing unit may use location information to obtain at least one name from among road names, intersection names, address names, and facility names of a location identified by a second identifying unit from the map information stored in the storage unit, and provide the obtained name as text content. This allows the information processing device to clearly indicate to the user the location (abnormality location) that meets predetermined conditions different from normal vehicle operation, thereby improving user convenience. Furthermore, the information processing device can present information as a search result when a user searches for information on a location that meets predetermined conditions (location where an anomaly occurred) (making it easier for users to find SNS posts when they search).

[0076] (Aspect 4) In one embodiment of the information processing device, the providing unit may provide text content and location as content if the current location of the vehicle obtained based on location information and the location identified by the second identifying unit are separated by a predetermined distance or more. This allows the information processing device to protect the privacy of users (vehicle drivers and passengers).

[0077] (Aspect 5) One embodiment of the information processing device includes: an acquisition unit that acquires vehicle information generated by a vehicle; a first identification unit that identifies vehicle driving that meets predetermined conditions, based on the vehicle information acquired by the acquisition unit, which are presumed to be different from normal vehicle driving and indicate that an abnormality or vehicle driving has occurred on the road; a second identification unit that, when the first identification unit identifies vehicle driving that meets the predetermined conditions, identifies an abnormality location as the location that meets those predetermined conditions, based on location information included in the vehicle information; and a provision unit that provides, over a communication network, text content indicating that an abnormality has occurred on the road, corresponding to the vehicle driving that meets the predetermined conditions identified by the first identification unit, and the abnormality location identified by the second identification unit. As a result, the information processing device can achieve the same effects as the information processing device of the embodiment described above.

[0078] (Aspect 6) In one embodiment of the information processing method, a computer performs the following steps: an acquisition step of acquiring vehicle information generated by a vehicle; a first identification step of identifying vehicle driving that falls under predetermined conditions different from normal vehicle driving based on the vehicle information acquired in the acquisition step; a second identification step of identifying a location that falls under those predetermined conditions based on location information included in the vehicle information, when the first identification step identifies vehicle driving that falls under the predetermined conditions; and a provision step of providing text content corresponding to the vehicle driving that falls under the predetermined conditions identified in the first identification step and the location identified in the second identification step as content. As a result, the information processing method can achieve the same effects as the information processing apparatus of the above-described embodiment.

[0079] (Aspect 7) One embodiment of an information processing program enables a computer to implement: an acquisition function for acquiring vehicle information generated by a vehicle; a first identification function for identifying vehicle driving that falls under predetermined conditions different from normal vehicle driving, based on the vehicle information acquired by the acquisition function; a second identification function for identifying a location that falls under those predetermined conditions, based on location information included in the vehicle information, when the first identification function identifies vehicle driving that falls under those predetermined conditions; and a provision function for providing text content corresponding to the vehicle driving that falls under the predetermined conditions identified by the first identification function, and the location identified by the second identification function, as content. As a result, the information processing program can achieve the same effects as the information processing apparatus of the above-described embodiment. [Explanation of Symbols]

[0080] 100 Information Processing Devices 110 Control Unit 111 Acquisition Department 112 1st Specific Part 113 Second Specific Part 114 Estimation Department 115 Provision Department 121 Communications Department 122 Storage section 123 Display section 210 vehicles 220 servers 300 road 311. Sudden braking of a vehicle (vehicles operating under specified conditions) 312. U-turn of a vehicle (vehicles that meet the specified conditions) 321 Location that meets the specified conditions (location where an anomaly occurred)

Claims

1. An acquisition unit that acquires vehicle information generated by the vehicle, Based on the vehicle information acquired by the acquisition unit, a first identification unit identifies the driving of a vehicle that falls under predetermined conditions different from the normal driving of a vehicle, When the first identification unit identifies the movement of a vehicle that meets predetermined conditions, the second identification unit identifies the location that meets the predetermined conditions based on the location information included in the vehicle information, A providing unit that provides text content corresponding to the driving of a vehicle that meets predetermined conditions specified by the first specifying unit, and a location specified by the second specifying unit, An information processing device equipped with the following features.

2. Based on the vehicle information acquired by the acquisition unit, the estimation unit estimates the movement of the vehicle at the location identified by the second identification unit, When the providing unit estimates the movement of a vehicle at the location using the estimation unit, it provides that the situation described in the content has ended, in relation to the content provided at a time earlier than the estimation. The information processing apparatus according to claim 1.

3. It is equipped with a memory unit that stores map information related to road maps, The providing unit uses the location information to obtain at least one name from among the road name, intersection name, address name, and facility name of the location identified by the second identifying unit from the map information stored in the storage unit, and provides the obtained name in the text content. The information processing apparatus according to claim 1.

4. The providing unit provides the text content and the location as content if the current location of the vehicle obtained based on the location information and the location identified by the second identifying unit are separated by a predetermined distance or more. The information processing apparatus according to claim 1.

5. An acquisition unit that acquires vehicle information generated by the vehicle, Based on the vehicle information acquired by the acquisition unit, a first identification unit identifies vehicle movements that meet predetermined conditions, which are presumed to indicate an abnormality on the road or vehicle movement that differs from normal vehicle movement. When the first identification unit identifies the movement of a vehicle that meets predetermined conditions, the second identification unit identifies the location of the abnormality as the location that meets the predetermined conditions based on the location information included in the vehicle information, A providing unit that provides, over a communication network, text content indicating that an abnormality has occurred on the road, corresponding to the driving of a vehicle that meets predetermined conditions identified by the first identifying unit, and the location where the abnormality occurred, identified by the second identifying unit. An information processing device equipped with the following features.

6. Computers Acquisition step to obtain vehicle information generated by the vehicle, Based on the vehicle information obtained in the acquisition step, a first identification step identifies the driving of a vehicle that falls under predetermined conditions different from the driving of a normal vehicle, When the first identification step identifies the movement of a vehicle that meets predetermined conditions, the second identification step identifies the location that meets the predetermined conditions based on the location information included in the vehicle information, A providing step that provides text content corresponding to the driving of a vehicle that meets predetermined conditions identified by the first identifying step, and a location identified by the second identifying step, An information processing method that performs the following.

7. On the computer, A function to acquire vehicle information generated by the vehicle, Based on the vehicle information acquired by the aforementioned acquisition function, a first identification function identifies the driving of a vehicle that falls under predetermined conditions different from the driving of a normal vehicle, When the first identification function identifies the movement of a vehicle that meets predetermined conditions, a second identification function identifies the location that meets the predetermined conditions based on the location information included in the vehicle information, A providing function that provides text content corresponding to the driving of a vehicle that meets predetermined conditions identified by the first specific function, and a location identified by the second specific function, An information processing program that makes this possible.

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