Information Processing Apparatus, Information Processing Method, and Information Processing Program

Abstract

To provide an information processing device capable of estimating the presence of obstacles, an information processing method, and an information processing program.SOLUTION: The information processing device includes: a first acquisition unit that acquires a piece of vehicle information including image information about images generated by the on-board camera; a first estimation unit that estimates the lanes in a road based on the image information acquired by the first acquisition unit; and a second estimation unit that estimates whether obstacles exist between the lane estimated by the first estimation unit and a predetermined distance in the road width direction.SELECTED DRAWING: Figure 1

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] For example, when there is an obstacle such as a parked vehicle in front of the lane in which the host vehicle is traveling, the driver of the vehicle may need to perform a driving operation to avoid the obstacle. In this case, if the driver knows in advance the presence of the obstacle, they may be able to perform a driving operation with a margin and may be able to avoid the occurrence of an accident or the like.

[0003] Patent Document 1 discloses an apparatus that supports the driving of the host vehicle by recognizing an obstacle that closes the front of the lane in which the host vehicle is traveling. The apparatus acquires point cloud data of the surrounding environment by a LiDAR mounted on the host vehicle and recognizes the presence of an obstacle. In this case, the apparatus recognizes a region where the lane is blocked by a parked vehicle that is an obstacle to the driving of the host vehicle.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] As described above, the apparatus disclosed in Patent Document 1 recognizes an obstacle by obtaining point cloud data using a LiDAR. However, LiDARs are not installed in many vehicles. In vehicles without a LiDAR, obstacles cannot be recognized. Therefore, it is desired to estimate the presence of an obstacle using equipment installed in more vehicles.

[0006] The present disclosure provides an information processing apparatus, an information processing method, and an information processing program capable of estimating the presence of an obstacle.

Means for Solving the Problem

[0007] An information processing apparatus according to one aspect includes a first acquisition unit that acquires vehicle information including image information regarding an image generated by an in-vehicle camera, a first estimation unit that estimates lanes marked on a road based on the image information acquired by the first acquisition unit, and a second estimation unit that estimates whether an obstacle exists within a predetermined distance in the road width direction from the lanes estimated by the first estimation unit.

Effect of the Invention

[0008] According to one aspect, it is possible to estimate whether an obstacle exists in the traveling direction of the vehicle by using the image information regarding the image generated by the in-vehicle camera.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Mode for Carrying Out the Invention

[0010] Hereinafter, one embodiment will be described.

[0011] [Outline of Information Processing System 1] First, the outline of an information processing system 1 according to one embodiment will be described. FIG. 1 is a diagram for explaining an information processing system 1 according to an embodiment.

[0012] The information processing system 1 includes, for example, a vehicle 10, a server 20, an information processing device 30, and the like. The information processing device 30 may be configured as, for example, an obstacle estimation device that estimates whether there is an obstacle in the traveling direction of the vehicle 10. Further, the information processing device 30 may be configured as, for example, a traffic flow estimation device that estimates whether there is a disruption in the traffic flow in the traveling direction of the vehicle 10. Further, the information processing device 30 may be configured as, for example, a map registration device that registers the result of the above-described estimation in a road map. The information processing device 30 may be, for example, a server, a desktop, a laptop, a tablet, a smartphone, or the like.

[0013] The information processing device 30 acquires vehicle information generated by the vehicle 10. As an example in this case, the information processing device 30 may acquire vehicle information from at least one selected from the group of the server 20 and the vehicle 10. Here, when the vehicle 10 generates vehicle information, for example, the vehicle 10 may transmit the vehicle information to the server 20 or the information processing device 30. The vehicle information may include, for example, image information generated by imaging the traveling direction by an imaging unit (in-vehicle camera) mounted on the vehicle 10. Further, the vehicle information may include information regarding the result of recognizing an object around the own vehicle by a sensor (in-vehicle sensor) mounted on the vehicle 10. As an example in this case, the object may be a ground feature, and the information may be ground feature recognition information. As another example, the in-vehicle sensor may use laser light to recognize an object (ground feature). Further, the vehicle information may include, for example, vehicle travel information regarding the travel of the vehicle 10. The vehicle travel information may be, for example, probe information, CAN information, or the like. Further, the vehicle information may include, for example, position information regarding the travel position of the vehicle 10. As an example in this case, the vehicle 10 may acquire position information using GNSS or the like. In addition, the vehicle information may include, for example, time information regarding time.

[0014] The information processing device 30 estimates the lane attached to the road on which the vehicle 10 travels by using the image information. The lane may be, for example, the center line of the road lane when the road has one lane on one side, or may be the center line of the road lane or the lane boundary line when the road has two or more lanes on one side. Here, the lane may be, for example, the lane boundary line of the first traffic lane when there are two or more lanes on one side.

[0015] The information processing device 30 estimates the presence of an obstacle within a predetermined distance in the road width direction (direction from the center of the road to the outside) from the lane estimated as described above. In this case, the information processing device 30 may estimate the presence of an obstacle based on the image information. The obstacle may be, for example, a parked vehicle and a ground object.

[0016] [Details of Information Processing Device 30] Next, the information processing device 30 according to an embodiment will be described in detail. FIG. 2 is a block diagram for explaining the information processing device 30 according to an embodiment.

[0017] The information processing device 30 includes, for example, a communication unit 331, a storage unit 332, a display unit 333, and a control unit 311. The communication unit 331, the storage unit 332, and the display unit 333 may be an embodiment of an output unit. The control unit 311 includes, for example, a first acquisition unit 312, a second acquisition unit 313, a third acquisition unit 314, a first estimation unit 315, a second estimation unit 316, a third estimation unit 317, a registration unit 318, a route guidance unit 319, and an output control unit 320. The control unit 311 may be configured by, for example, an arithmetic processing device of the information processing device 30. The control unit 311 (for example, an arithmetic processing device) may realize the functions of each unit (for example, the first acquisition unit 312, the second acquisition unit 313, the third acquisition unit 314, the first estimation unit 315, the second estimation unit 316, the third estimation unit 317, the registration unit 318, the route guidance unit 319, and the output control unit 320) by appropriately reading and executing various programs stored in the storage unit 332.

[0018] The communication unit 331 can transmit and receive various information to and from, for example, a device (external device) outside the information processing device 30. The external device may be, for example, the server 20 and the vehicle 10, etc.

[0019] The storage unit 332 may store, for example, various information and programs. An example of the storage unit 332 may be a memory, a solid state drive, a hard disk drive, etc. Note that the storage unit 332 may be, for example, a storage area and a server on the cloud, etc. Also, the storage unit 332 may store map information regarding a road map. The map information may record, for example, position information such as longitude and latitude.

[0020] The display unit 333 can display, for example, various characters, symbols, images, etc.

[0021] The first acquisition unit 312 may acquire vehicle information from the vehicle 10 or the server 20 via the communication unit 331, for example. In this case, the first acquisition unit 312 acquires vehicle information including image information regarding an image generated by an in-vehicle camera. The image information is information generated by a camera (in-vehicle camera) mounted on the vehicle 10, for example. In this case, the in-vehicle camera may image, for example, the traveling direction of the vehicle 10, etc.

[0022] The first acquisition unit 312 may acquire vehicle information including ground object recognition information regarding a ground object recognized based on the measurement result of an in-vehicle sensor. The in-vehicle sensor may recognize a ground object, for example, by irradiating laser light and receiving the laser light (reflected light) reflected by the ground object. An example of the in-vehicle sensor may be a sensor that generates point cloud data, etc.

[0023] The first acquisition unit 312 may acquire vehicle information including vehicle traveling information regarding the traveling of the vehicle 10. The vehicle traveling information may be, for example, information regarding the driving operation of the vehicle 10, or may be information acquired by various sensors and devices mounted on the vehicle 10, etc.

[0024] The first acquisition unit 312 may acquire vehicle information including position information regarding the traveling position of the vehicle 10. The position information may be, for example, information regarding the position acquired by the vehicle 10 using GNSS or the like.

[0025] The second acquisition unit 313 may acquire the traveling state of the vehicle 10 in at least one of the following cases (1) to (4) based on the vehicle traveling information acquired by the first acquisition unit 312. (1) When the steering angle toward the center of the road changes while the vehicle 10 is traveling (2) When there is an operation of the turn signal toward the center of the road while the vehicle 10 is traveling (3) When there is a change in which the traveling speed of the vehicle 10 decreases (4) When there is a change in which the inter-vehicle distance from the vehicle 10 traveling ahead or behind becomes narrow

[0026] Regarding the above (1) The change in the steering angle toward the center of the road while the vehicle 10 is traveling may be, for example, a change in the steering angle toward the center of the road with respect to the traveling direction of the vehicle 10. That is, for example, on a road where left-side driving of the vehicle 10 is specified (such as a road in Japan), it may be a change in the steering angle to the right. In this case, for example, when the vehicle 10 changes lanes to the right and when the vehicle 10 turns right, the steering angle of the vehicle 10 is different. That is, for example, the steering angle when the vehicle 10 changes lanes to the right is smaller than the steering angle when the vehicle 10 turns right. Also, for example, the steering angle when the vehicle 10 changes lanes falls within a substantially predetermined angle range. Therefore, for example, it is possible to prescribe in advance the range of the steering angle when the vehicle 10 changes lanes.

[0027] Regarding the above (2) The operation of the turn signal toward the center of the road can be acquired, for example, based on the operation direction of the turn signal (as an example, the operation of the turn signal to the right).

[0028] Regarding the above (3) The change in the running speed of the vehicle 10 becoming slower can be obtained, for example, based on the rotational speed of the tire's rotation axis or drive shaft, etc., the speed indicated by a speedometer mounted on the vehicle 10, and the presence or absence of brake operation by the driver, etc.

[0029] Regarding the above (4) The change in the inter-vehicle distance becoming narrower with the vehicle 10 traveling ahead or behind can be obtained, for example, by continuously or intermittently monitoring the inter-vehicle distance with the preceding vehicle or following vehicle over time using in-vehicle sensors (such as obstacle sensors as an example) that monitor the front and rear of the vehicle 10. Here, the in-vehicle sensor may be mounted on the host vehicle to obtain the inter-vehicle distance with the preceding vehicle or following vehicle, may be mounted on the preceding vehicle to obtain the inter-vehicle distance to the following host vehicle, or may be mounted on the following vehicle to obtain the inter-vehicle distance to the preceding host vehicle. Note that the above-described first acquisition unit 312 may obtain vehicle travel information (vehicle information) including the inter-vehicle distance acquired by the host vehicle, the preceding vehicle, or the following vehicle, respectively, from each vehicle 10. Alternatively, the first acquisition unit 312 may obtain vehicle travel information (vehicle information) including the inter-vehicle distance acquired by the preceding vehicle or the following vehicle via the host vehicle.

[0030] The third acquisition unit 314 may obtain facility information regarding the location and business hours of facilities. The facilities may be various facilities such as restaurants and stores that sell goods or provide services, etc. The location of the facility may be, for example, a location based on the longitude and latitude, etc., regarding the site location of the facility. The facility information may be registered in map information, for example, or may be stored in a server 20, etc. Also, the third acquisition unit 314 may obtain, as facility information, the location and business hours of facilities publicly available on a communication network.

[0031] The first estimation unit 315 estimates the lanes assigned to the road based on the image information acquired by the first acquisition unit 312. For example, the first estimation unit 315 may estimate the lanes of the road recorded in the image information based on a learned model that has learned the shape, color, etc. of the lanes and the image information. Alternatively, the first estimation unit 315 may estimate the lanes of the road recorded in the image information by using, for example, pattern matching based on patterns such as the shape of the lanes.

[0032] In this case, the first estimation unit 315 may be able to estimate at least one of the lane boundary line and the center line of the lane as the lane. For example, when the road has one lane on one side, the lane may be the center line 401 of the lane (see FIG. 3), and when the road has two or more lanes on one side, it may be the center line 401 or the lane boundary line 402 of the lane (see FIG. 4). Here, for example, when there are two or more lanes on one side, the lane boundary line 402 may be the lane boundary line 402a of the first traffic lane 411. Furthermore, the first estimation unit 315 may estimate the outside lane line 403 (FIGS. 3 and 4) as the lane. That is, the first estimation unit 315 may estimate at least one of the lane boundary line 402 and the center line of the lane 401 and the outside lane line 403 as the lane.

[0033] The second estimation unit 316 estimates whether there is an obstacle between the lanes estimated by the first estimation unit 315 and up to a predetermined distance in the road width direction. In this case, the second estimation unit 316 may estimate whether there is an obstacle between the lane boundary line or the center line of the lane and up to a predetermined distance in the road width direction. That is, for example, the second estimation unit 316 may estimate whether there is an obstacle between the center line or the lane boundary line of the lane estimated by the first estimation unit 315 and up to a predetermined distance in the direction of the outside lane line (the left direction in Japanese roads).

[0034] FIG. 3 is a diagram for explaining an example of a road with one lane on one side. FIG. 4 is a diagram for explaining an example of a road with three lanes on one side.

[0035] The second estimation unit 316 may estimate whether there is an obstacle 421 between the center line 401 of the lane estimated by the first estimation unit 315, for example, and the outside lane line 403 within a predetermined distance L1, L2 in the direction from the center line 401 or the lane boundary line 402a of the first traffic lane 411 of a road with two or more lanes on one side (see FIG. 4). As a specific example in this case, the predetermined distances L1, L2 may be 3.5 m, or various other distances. Here, 3.5 m is an example, and it may be appropriately set in consideration of the width that is easy for the vehicle to travel.

[0036] Further, for example, when the lane estimated by the first estimation unit 315 is a lane of a road with two or more lanes on one side, the second estimation unit 316 may add the second traffic lane 412 to the above-described predetermined distance, and use the total distance including the lane widths of one or more lanes on the center line 401 side of the second traffic lane 412 as the predetermined distances L3, L4, and estimate whether there is an obstacle 421 between the lanes (lane boundary line 402b and center line 401) on the center line 401 side of the second traffic lane 412 and the predetermined distance (see FIG. 4). As a specific example, when the road estimated by the first estimation unit 315 is a road with two lanes on one side, the second estimation unit 316 may use the total distance obtained by adding the lane width of the second traffic lane 412 and the above-described exemplary 3.5 m to the center line 401 as the predetermined distance, and estimate whether there is an obstacle 421 between the center line 401 and the predetermined distance. As another specific example, when the road estimated by the first estimation unit 315 is a road with three lanes on one side, the second estimation unit 316 may use the total distance obtained by adding the lane width of the second traffic lane 412 and the above-described exemplary 3.5 m to the lane boundary line 402b between the second traffic lane 412 and the third traffic lane 413 as the predetermined distance L3, and estimate whether there is an obstacle 421 between the lane boundary line 402b and the predetermined distance L3 (see FIG. 4). As another specific example, when the road estimated by the first estimation unit 315 is a three-lane road on one side, the second estimation unit 316 sets the total distance obtained by adding the lane widths of the second traffic lane 412 and the third traffic lane 413, respectively, and the above-described exemplary 3.5 m from the center line 401 of the lane as a predetermined distance L4, and may estimate whether there is an obstacle 421 between the center line 401 of the lane and the predetermined distance L4 (see FIG. 4).

[0037] The obstacle may be, for example, a parked vehicle or a ground object that may pose an obstacle when the vehicle 10 travels on the road. The obstacle during the travel of the vehicle 10 may be, for example, a steering operation such as a lane change or a driving operation such as deceleration that requires an operation to avoid the obstacle.

[0038] The second estimation unit 316 may estimate whether there is an obstacle based on the image information acquired by the first acquisition unit 312. For example, the second estimation unit 316 may estimate whether an obstacle is recorded in the image information based on a learned model that has learned various obstacle patterns such as the shape of the vehicle and ground objects such as poles and trees, and the image information. Alternatively, the second estimation unit 316 may estimate whether an obstacle is recorded in the image information by using, for example, pattern matching based on patterns such as the shape of the lane and ground objects. In this case, when the image information is information recording a moving image or information recording still images continuously at predetermined time intervals, the second estimation unit 316 can estimate whether the object recorded in the image information is stationary or moving by using temporally different frames and still images. The second estimation unit 316 can estimate a stationary object as an obstacle by using a learned model, pattern matching, or the like.

[0039] The second estimation unit 316 may estimate whether there is an obstacle based on the ground object recognition information acquired by the first acquisition unit 312. That is, when a ground object is recognized based on, for example, the detection result of an in-vehicle sensor, the second estimation unit 316 may estimate whether there is an obstacle based on the recognized result (ground object recognition information).

[0040] When the outside lane line cannot be recognized, the second estimation unit 316 may estimate that there is an obstacle. That is, for example, when the outside lane line is estimated by the first estimation unit 315 and the outside lane line cannot be recognized in the image information, the second estimation unit 316 may estimate that there is an obstacle. In this case, for example, the second estimation unit 316 may estimate that there is an obstacle when the outside lane line can be recognized in the image at a certain time t but cannot be recognized in the images within a predetermined time after time t, where the images are frames and still images (pictures) at different times. Further, for example, based on a plurality of vehicle information at the same position (substantially the same position), when the outside lane line could be recognized by the first estimation unit 315 but cannot be recognized within a predetermined time from the time when the outside lane line was last recognized, and then can be recognized within a subsequent predetermined time, that is, when the outside lane line cannot be recognized temporarily by the first estimation unit 315, the second estimation unit 316 may estimate that there is an obstacle.

[0041] Alternatively, for example, based on a plurality of image information, when the outside lane line cannot be recognized in a specific time zone at the same position (the same area), the second estimation unit 316 may estimate that there is an obstacle. Further, for example, based on a plurality of image information, when the outside lane line cannot be recognized relatively for a long period at the same position (the same area), the second estimation unit 316 may estimate that the outside lane line cannot be recognized due to rubbing or removal, etc.

[0042] The second estimation unit 316 may estimate the presence of an obstacle based on the driving state of the vehicle 10 acquired by the second acquisition unit 313. That is, when the second acquisition unit 313 acquires the driving state of at least one vehicle 10 selected from the above groups (1) to (4), the second estimation unit 316 may estimate that there is an obstacle. Here, for example, the second estimation unit 316 may specify the position where the presence of an obstacle is estimated based on the position information included in the vehicle information.

[0043] For example, when it is estimated that there is a facility in operation within a predetermined distance from the traveling position of the vehicle 10 based on the position information acquired by the first acquisition unit 312 and the facility information acquired by the third acquisition unit 314, the second estimation unit 316 may estimate the presence of an obstacle. The second estimation unit 316 may acquire, for example, weather information regarding the weather when the vehicle 10 that generated vehicle information from the server 20 or the like travels. The second estimation unit 316 may specify, for example, the time when the obstacle exists based on time information (vehicle information). In this case, the second estimation unit 316 may, for example, aggregate the estimation results for each predetermined period, and specify an area where the presence of a plurality of obstacles is estimated (an area including positions where the presence of a predetermined number or more of obstacles is estimated) based on the day of the week, time zone, weather, and business status of neighboring facilities, etc. The second estimation unit 316 may specify the estimated area (position) for each link unit of the road network or for each section unit divided by a unit such as 100 m of the road. The second estimation unit 316 may specify the position where the presence of an obstacle is estimated based on, for example, the vehicle information acquired in real time and the vehicle information acquired in the past.

[0044] Note that in a section of a road where there is traffic control according to the day of the week, time zone, etc. such as a parking prohibition, it is conceivable that the vehicle may park outside the period of the traffic control. In this case, for example, when the second estimation unit 316 estimates the presence of an obstacle according to the day of the week and time zone described above, it is also possible to estimate the presence or absence of traffic control such as a parking prohibition according to the day of the week and time zone.

[0045] Further, the second estimation unit 316 may not estimate the presence of an obstacle within an intersection based on the position information (vehicle information) and the map information.

[0046] The third estimation unit 317 may estimate the disturbance of the traffic flow based on the driving state of the vehicle 10 acquired by the second acquisition unit 313. That is, when the third estimation unit 317 acquires the driving state of at least one vehicle 10 selected from the above groups (1) to (4) by the second acquisition unit 313, it may estimate that there is a disturbance in the traffic flow. Here, the traffic flow may be, for example, the overall flow of a plurality of vehicles or the like obtained by aggregating the movements of a plurality of vehicles on a road. The disturbance of the traffic flow may be the case when at least one of the traffic volume becomes relatively large, the traffic density becomes relatively high, and the driving speed becomes relatively slow, which is different from the case where a plurality of vehicles or the like travel smoothly on the road.

[0047] When there is a change amount or a driving state equal to or greater than an operation as a threshold value in the driving state of the vehicle 10 acquired by the second acquisition unit 313, the third estimation unit 317 may estimate that there is a disturbance in the traffic flow. For example, when the third estimation unit 317 acquires the driving state of the vehicle 10 in which the steering angle changes in the central direction of the road when the vehicle 10 travels (the change in the steering angle when the vehicle 10 changes lanes) by the second acquisition unit 313, if there is a change in the steering angle greater than the steering angle of the steering wheel when the vehicle 10 travels along the road (travels straight), it may be estimated that there is a disturbance in the traffic flow. Here, for example, it is considered that the steering angle when the vehicle 10 changes lanes is larger than the steering angle when the vehicle 10 travels along the road. Therefore, the upper limit of the change in the steering angle when the vehicle 10 travels along the road may be set as the threshold value A. Also, for example, it is considered that the steering angle of the vehicle 10 when the vehicle 10 turns right (or turns left) is larger than the steering angle when the vehicle 10 changes lanes. Therefore, an upper limit threshold value B (threshold value A < threshold value B) may also be set for the steering angle when the vehicle 10 changes lanes, and the range between the two threshold values A and B may be used as the change in the steering angle when the vehicle 10 changes lanes. Here, the threshold values A and B may be set in advance.

[0048] Further, for example, when the second acquisition unit 313 acquires the operation of the direction indicator in the central direction of the road when the vehicle 10 is traveling ((2) the operation of the direction indicator when the vehicle 10 changes lanes), the third estimation unit 317 may estimate that there is a disturbance in the traffic flow based on the operation of the direction indicator (turn signal on).

[0049] Further, for example, when the second acquisition unit 313 acquires the driving state of the vehicle 10 in which the driving speed of the vehicle 10 decreases ((3)), if the driving speed decreases below the threshold based on the driving speed immediately before deceleration, etc., the third estimation unit 317 may estimate that there is a disturbance in the traffic flow. Here, the threshold may be set in advance.

[0050] Further, for example, when the second acquisition unit 313 acquires the driving state of the vehicle 10 in which the distance between the vehicle 10 traveling in front or behind becomes narrow ((4)), if the distance between the vehicles becomes less than or equal to the threshold based on the distance between the vehicles before the change in the distance between the vehicles becoming narrow, the third estimation unit 317 may estimate that there is a disturbance in the traffic flow. Here, the threshold may be set in advance.

[0051] Note that the third estimation unit 317 may estimate that there is a disturbance in the traffic flow based on at least one of the driving states (1) to (4) as described above. In this case, the third estimation unit 317 may estimate that there is a disturbance in the traffic flow when, for example, it corresponds to one of the following (A) to (C). (A) When one of the driving states (1) to (4) is acquired (B) When two (or three) of the driving states (1) to (4) are acquired (C) When all of the driving states (1) to (4) are acquired

[0052] If it is estimated that there is a facility in operation within a predetermined distance from the traveling position of the vehicle 10 based on the position information acquired by the first acquisition unit 312 and the facility information acquired by the third acquisition unit 314, the third estimation unit 317 may estimate that there is a disruption in the traffic flow. The third estimation unit 317 identifies a position at which at least one selected from the above groups (1) to (4) is acquired by the first acquisition unit 312 based on the position information (vehicle information). Further, the third estimation unit 317 may identify, for example, whether a facility is located within a predetermined distance from the identified position based on the identified position and the facility information (facility position). Furthermore, the third estimation unit 317 may estimate, for example, whether the facility corresponding to the identified location is in operation based on the time information (facility information) and the facility information (business hours of the facility). If it is estimated that the facility corresponding to the identified location is in operation, the third estimation unit 317 may estimate that there is a disruption in the traffic flow.

[0053] The third estimation unit 317 may acquire, for example, weather information regarding the weather when the vehicle 10 that generated the vehicle information traveled from the server 20 or the like. The third estimation unit 317 may identify, for example, the time when there is a disruption in the traffic flow based on the time information (vehicle information). In this case, the third estimation unit 317 may, for example, aggregate the estimation results for each predetermined period and identify an area where a disruption in a plurality of traffic flows is estimated (an area including positions where a disruption in a traffic flow is estimated to be a predetermined number or more) based on the day of the week, time zone, weather, and business status of neighboring facilities. The third estimation unit 317 may identify, for example, the position where a disruption in the traffic flow is estimated for each link of the road network or for each section divided in units of 100 m or the like of the road. The third estimation unit 317 may identify, for example, the position where the presence of an obstacle is estimated based on the vehicle information acquired in real time and the vehicle information acquired in the past.

[0054] Note that when the third estimation unit 317 estimates a disruption in the traffic flow according to the day of the week and time zone described above, it is also possible to estimate the presence or absence of traffic regulations such as parking bans according to the day of the week and time zone. Further, the third estimation unit 317 does not have to estimate the presence of an obstacle within an intersection based on the position information (vehicle information) and the map information.

[0055] When the presence of an obstacle is estimated by the second estimation unit 316 and / or when it is estimated by the third estimation unit 317 that there is a disruption in the traffic flow, the registration unit 318 may register the position based on the estimation in the map information. That is, the registration unit 318 may register, for example, the position where the presence of an obstacle is estimated in the road map (map information). Further, the registration unit 318 may register, for example, the position where a disruption in the traffic flow is estimated in the road map (map information).

[0056] The registration unit 318 may rank the ease of running of the vehicle 10 based on the number of cases where the presence of an obstacle is estimated and the number of cases where a disruption in the traffic flow is estimated, and also register this ranking in association with the estimated position in the road map (map information). For example, when the above-mentioned number of cases is relatively large, the registration unit 318 may rank the vehicle 10 as being more difficult to run. Further, for example, when the above-mentioned number of cases is relatively small, the registration unit 318 may rank the vehicle 10 as being easier to run.

[0057] In this case, the registration unit 318 may rank the ease of running of the vehicle 10 according to the number of lanes (number of lanes on one side) of the road at the position where the presence of an obstacle is estimated and the position where a disruption in the traffic flow is estimated, and also register this ranking in association with the estimated position in the road map (map information). For example, when the above-mentioned number of lanes is relatively small, the registration unit 318 may rank the vehicle 10 as being more difficult to run. Further, for example, when the above-mentioned number of lanes is relatively large, the registration unit 318 may rank the vehicle 10 as being easier to run.

[0058] Further, the registration unit 318 may rank the ease of driving of the vehicle 10 according to the position where the presence of an obstacle is estimated and the width of the road (for example, the width of the first traffic lane, etc.) at the position where the traffic flow is estimated to be disturbed, and register the ranking in association with the estimated position in the road map (map information). For example, when the road width described above is relatively narrow, the registration unit 318 may rank the vehicle 10 as being more difficult to drive. Also, for example, when the road width described above is relatively wide, the registration unit 318 may rank the vehicle 10 as being easier to drive.

[0059] Further, the registration unit 318 may register the estimated results (the presence of obstacles and the disturbance of the traffic flow) according to the day of the week, time zone, weather, and business status of the facility in the road map (map information). Thereby, the registration unit 318 can provide information to a user (for example, a driver, etc.) who uses the road map (map information) on whether it is constantly difficult to drive the vehicle 10 or temporarily difficult to drive the vehicle 10.

[0060] The route guidance unit 319 may perform route guidance from the departure point to the arrival point using a road map in which at least one of the position where the presence of an obstacle is estimated and the position where the traffic flow is disrupted is registered. The route guidance unit 319 may be, for example, a navigation function or the like. For example, when the route guidance unit 319 receives a request for route guidance from the departure point to the destination (route guidance request), it may search for a detour route that avoids the position where the presence of an obstacle is estimated and the position where the traffic flow is disrupted, and perform route guidance using the detour route. Also, for example, when the route guidance unit 319 receives a route guidance request, it may perform route guidance to travel in a lane that avoids the position where the presence of an obstacle is estimated and the position where the traffic flow is disrupted (for example, in a road with at least two lanes on one side, other traffic lanes excluding the first traffic lane). Further, for example, when the registration unit 318 registers the ranking of the ease of driving the vehicle 10 in the road map (map information), the route guidance unit 319 may perform route guidance to travel on a road (or a lane) in which a ranking indicating easier driving of the vehicle 10 is registered. Here, the route guidance unit 319 may receive a route guidance request from a user terminal (not shown) via the communication unit 331. The user terminal may be, for example, a terminal used by the user of the information processing device 30. In this case, the user terminal may be, for example, a portable terminal (as an example, a laptop, a tablet, and a smartphone, etc.). The route guidance unit 319 may output information regarding route guidance to the user terminal via the communication unit 331, for example.

[0061] When vehicle information is acquired in real time, for example, the route guidance unit 319 may acquire information on the ease or difficulty of running of the vehicle 10 estimated using the vehicle information in almost real time and perform route guidance. In this case, the control unit 311 (for example, the first to third estimation units 315 to 317, etc.) may perform estimation processing using the information obtained from the current situation information without performing statistical estimation processing using the vehicle running information. As an example, the route guidance unit 319 may acquire a guidance route based on using statistical estimation processing in route search when the vehicle 10 departs, and perform route guidance taking into account the above-described almost real-time estimation processing in route guidance after the vehicle 10 departs. That is, the route guidance unit 319 may guide, for example, a lane that is easy to drive on based on estimation in route guidance after departure. Further, the route guidance unit 319 may, for example, in route guidance after departure, acquire the detection result of a sensor (in-vehicle sensor) mounted on the own vehicle and guide a lane that is easy to drive on according to the almost real-time estimation result based on the result.

[0062] Note that when vehicle information is acquired in real time, for example, the route guidance unit 319 may perform route guidance using the estimation result registered in the road map in almost real time by the registration unit 318. In this case, the registration unit 318 may register, for example, the degree of ease of running of the vehicle 10 in the road map (map information) with respect to the link cost of route search. The route guidance unit 319 may present, for example, a guidance route candidate that prioritizes the degree of ease of running of the vehicle 10.

[0063] In addition, the route guidance unit 319 can provide route guidance for both manned vehicles and unmanned vehicles, for example. In this case, when the vehicle 10 is set to the automatic driving mode, the driving support mode, etc., the route guidance unit 319 may change the guidance route according to the degree of running ease ranked and registered in the road map (map information) by the registration unit 318. Further, when the vehicle 10 is set to the automatic driving mode, the driving support mode, etc., the route guidance unit 319 may select each control mode such as the automatic driving mode and the driving support mode according to the degree of running ease ranked and registered in the road map (map information) by the registration unit 318, and may also switch between these control modes.

[0064] In addition, the route guidance unit 319 may provide guidance when driving on a road by using, for example, a road map in which at least one of the position where an obstacle is estimated to exist and the position where the traffic flow is disrupted is registered. For example, when the vehicle 10 travels along the road, the route guidance unit 319 may estimate whether there is an obstacle on the planned route (road) by using the road map of the planned route. That is, the route guidance unit 319 may estimate whether there is a disruption in the traffic flow on the planned route by using the road map, for example. When it is estimated that there is an obstacle (a disruption in the traffic flow) on the planned route (road), the route guidance unit 319 may present an easy-to-drive lane, for example. That is, the route guidance unit 319 may guide the vehicle to take avoidance measures such as lane change in advance before reaching the position where there is an obstacle (the position where the traffic flow is disrupted), for example. The route guidance unit 319 may provide the above-described guidance whether the vehicle 10 is a manned vehicle or an unmanned vehicle. As an example, the route guidance unit 319 may control the running of the vehicle so as to take avoidance measures such as lane change in advance, and may also provide driving support for the running of the vehicle.

[0065] The route guidance unit 319 may provide route guidance by using, for example, characters, symbols, images, sounds, etc.

[0066] The output control unit 320 may control the output unit to output the presence of an obstacle being estimated by the second estimation unit 316 and the estimated position thereof. The output control unit 320 may control the output unit to output the presence of traffic flow disturbance being estimated by the third estimation unit 317 and the estimated position thereof. The output control unit 320 may control the output unit to output a road map whose position has been registered by the registration unit 318. Here, the output unit may be, for example, the communication unit 331, the storage unit 332, the display unit 333, or the like.

[0067] That is, the output control unit 320 may control the communication unit 331 to transmit at least one selected from the group of the above-described estimation results and positions, and road maps to an external device. Here, the external device may be, for example, the server 20, the vehicle 10, a user terminal (not shown), or the like. In this case, the user terminal may be, for example, a desktop, a laptop, a tablet, a smartphone, or the like. Further, the output control unit 320 may control the storage unit 332 to store at least one selected from the group of the above-described estimation results and positions, and road maps. Further, the output control unit 320 may control the display unit 333 to display at least one selected from the group of the above-described estimation results and positions, and road maps.

[0068] [Information Processing Method] Next, an information processing method according to an embodiment will be described. FIG. 5 is a flowchart for explaining an information processing method according to an embodiment.

[0069] In step ST101, the first acquisition unit 312 acquires vehicle information. The vehicle information may include at least one piece of information selected from the group of, for example, image information, ground object recognition information, vehicle driving information, and position information.

[0070] In step ST102, the first estimation unit 315 estimates the lanes assigned to the road based on the image information acquired in step ST101. In this case, the first estimation unit 315 may estimate at least one of the lane boundary line and the center line of the lane as the lane. Also, the first estimation unit 315 may estimate at least one of the lane boundary line and the center line of the lane and the outer line of the lane as the lane.

[0071] In step ST103, the second estimation unit 316 estimates whether there is an obstacle between the lanes (for example, the lane boundary line or the center line of the lane, etc.) estimated in step ST102 and a predetermined distance in the road width direction. In this case, the second estimation unit 316 may estimate whether there is an obstacle based on the image information acquired in step ST101. Also, the second estimation unit 316 may estimate whether there is an obstacle based on the ground object recognition information in step ST101. When the outer line of the lane cannot be recognized, the second estimation unit 316 may estimate that there is an obstacle.

[0072] Here, the second acquisition unit 313 may acquire the driving state of the vehicle 10 in at least one of the following cases (1) to (4) based on the vehicle driving information acquired in step ST101. (1) When the steering angle changes in the central direction of the road while the vehicle 10 is driving (2) When there is an operation of the direction indicator in the central direction of the road while the vehicle 10 is driving (3) When there is a change in which the driving speed of the vehicle 10 decreases (4) When there is a change in which the inter-vehicle distance with the vehicle 10 driving in front or behind becomes narrow The second estimation unit 316 may estimate the presence of an obstacle based on the driving state of the vehicle 10 acquired by the second acquisition unit 313.

[0073] In step ST104, the third estimation unit 317 may estimate the disturbance of the traffic flow based on the driving state of the vehicle 10 acquired by the second acquisition unit 313. When the driving state of the vehicle 10 acquired by the second acquisition unit 313 has a change amount or an operation as a threshold value or more, the third estimation unit 317 may estimate that there is a disturbance in the traffic flow.

[0074] Here, the third acquisition unit 314 may acquire facility information. In this case, when it is estimated based on the position information acquired in step ST101 and the facility information acquired by the third acquisition unit 314 that there is a facility in operation within a predetermined distance from the driving position of the vehicle 10, the third estimation unit 317 may estimate that there is a disturbance in the traffic flow.

[0075] In step ST105, when the presence of an obstacle is estimated in step ST103 and / or when it is estimated that there is a disturbance in the traffic flow in step ST104, the registration unit 318 registers the position based on the estimation in the map information.

[0076] In step ST106, the route guidance unit 319 performs route guidance from the departure point to the arrival point using the road map in which at least one of the estimation results (e.g., position, etc.) of step 103 and step ST104 in step ST105 is registered. Note that, depending on the embodiment, the process of step ST106 may not be performed.

[0077] Each part of the above-described information processing apparatus 30 may be realized as a function of a computer's arithmetic processing unit or the like. That is, the first acquisition unit 312, the second acquisition unit 313, the third acquisition unit 314, the first estimation unit 315, the second estimation unit 316, the third estimation unit 317, the registration unit 318, the route guidance unit 319, and the output control unit 320 (control unit 311) of the information processing apparatus 30 may be realized as a first acquisition function, a second acquisition function, a third acquisition function, a first estimation function, a second estimation function, a third estimation function, a registration function, a route guidance function, and an output control function (control function) by a computer's arithmetic processing unit or the like, respectively. The information processing program can cause a computer to realize each of the above-described functions. The information processing program may be recorded on a non-transitory computer-readable recording medium such as a memory, a solid state drive, a hard disk drive, or an optical disk. Also, as described above, each part of the information processing apparatus 30 may be realized by an arithmetic processing unit or the like of a computer. The arithmetic processing unit or the like is configured by, for example, an integrated circuit or the like. For this reason, each part of the information processing apparatus 30 may be realized as a circuit constituting the arithmetic processing unit or the like. That is, the first acquisition unit 312, the second acquisition unit 313, the third acquisition unit 314, the first estimation unit 315, the second estimation unit 316, the third estimation unit 317, the registration unit 318, the route guidance unit 319, and the output control unit 320 (control unit 311) of the information processing apparatus 30 may be realized as a first acquisition circuit, a second acquisition circuit, a third acquisition circuit, a first estimation circuit, a second estimation circuit, a third estimation circuit, a registration circuit, a route guidance circuit, and an output control circuit (control circuit) constituting an arithmetic processing unit or the like of a computer. Also, the communication unit 331, the storage unit 332, and the display unit 333 (output unit) of the information processing apparatus 30 may be realized as a communication function, a storage function, and a display function (output function) including functions such as an arithmetic processing unit, for example. Also, the communication unit 331, the storage unit 332, and the display unit 333 (output unit) of the information processing apparatus 30 may be realized as a communication circuit, a storage circuit, and a display circuit (output circuit) by being configured by, for example, an integrated circuit or the like. Also, the communication unit 331, the storage unit 332, and the display unit 333 (output unit) of the information processing apparatus 30 may be configured as a communication device, a storage device, and a display device (output device) by being configured by, for example, a plurality of devices.

[0078] The information processing apparatus 30 can combine one or any plurality of the above-described plurality of parts. In the present disclosure, the term "information" is used, but the term "information" can be replaced with "data", and the term "data" can be replaced with "information".

[0079] [Aspects and Effects of the Present Embodiment] Next, an aspect of the present embodiment and the effects achieved by each aspect will be described. Note that the present embodiment is not limited to the aspects described below, and may be implemented by appropriately combining the above-described respective parts. Also, the effects described below are examples, and the effects achieved by each aspect are not limited to those described below. Further, each aspect may achieve at least one of the effects described below, for example.

[0080] (Aspect 1) An information processing apparatus according to one aspect includes: a first acquisition unit that acquires vehicle information including image information regarding an image generated by an in-vehicle camera; a first estimation unit that estimates a lane marked on a road based on the image information acquired by the first acquisition unit; and a second estimation unit that estimates whether an obstacle exists within a predetermined distance in the road width direction from the lane estimated by the first estimation unit. The information processing apparatus can estimate the presence of an obstacle by using the image information generated by the in-vehicle camera. In recent years, in-vehicle cameras have been used for driving recorders and Vlog (Video Blog) shooting, etc., and are being mounted on more vehicles. Therefore, since the information processing apparatus acquires image information by using an in-vehicle camera, which is a more general-purpose device, for example, it becomes possible to acquire more image information, and the presence of an obstacle can be estimated not only on roads with relatively high traffic volume but also on roads with relatively low traffic volume. By estimating the presence of an obstacle, the information processing apparatus can provide driving assistance for both manned and unmanned vehicles.

[0081] (Aspect 2) In an information processing apparatus according to one aspect, the second estimation unit may estimate whether an obstacle exists based on the image information acquired by the first acquisition unit. Thereby, the information processing apparatus can estimate the presence of an obstacle by using the image information generated by the in-vehicle camera.

[0082] (Aspect 3) In an information processing apparatus according to one aspect, a first acquisition unit acquires vehicle information including feature recognition information regarding a feature recognized based on a measurement result of an in-vehicle sensor, and a second estimation unit may estimate whether an obstacle exists based on the feature recognition information acquired by the first acquisition unit. As a result, since the information processing apparatus further uses the recognition result of the feature by the in-vehicle sensor, it is possible to more accurately estimate the presence of an obstacle. Further, when the information processing apparatus estimates the presence of an obstacle by further using the feature recognition information, it is possible to reduce the degree of dependence on the image analysis process using the image information, and reduce the processing load.

[0083] (Aspect 4) In an information processing apparatus according to one aspect, a first estimation unit can estimate at least one of a lane boundary line and a center line of a lane as a lane, and a second estimation unit may estimate whether an obstacle exists between the lane boundary line or the center line of the lane and a predetermined distance in the road width direction. As a result, the information processing apparatus can estimate the presence of an obstacle based on the lane boundary line and the center line of the lane.

[0084] (Aspect 5) In an information processing apparatus according to one aspect, a first estimation unit estimates at least one of a lane boundary line and a center line of a lane and an outer lane line as a lane, and a second estimation unit may estimate that an obstacle exists when the outer lane line cannot be recognized. When the outer lane line cannot be recognized, it can be estimated that a vehicle (including a two-wheeled vehicle) is parked on the outer lane line or a feature such as a planting exists. For this reason, since the information processing apparatus estimates that an obstacle exists when the outer lane line cannot be further recognized, it is possible to perform a more accurate estimation.

[0085] (Aspect 6) In an information processing apparatus according to one aspect, a first acquisition unit acquires vehicle information including vehicle travel information related to the travel of a vehicle. Based on the vehicle travel information acquired by the first acquisition unit, when the steering angle toward the center of the road changes while the vehicle is traveling, when there is an operation of a direction indicator toward the center of the road while the vehicle is traveling, when there is a change in which the travel speed of the vehicle decreases, and when there is a change in which the inter-vehicle distance from a vehicle traveling ahead or behind becomes narrow, a second acquisition unit acquires the travel state of at least one vehicle. A second estimation unit may estimate the presence of an obstacle based on the travel state of the vehicle acquired by the second acquisition unit. When there is an obstacle, it is considered that the driver of the vehicle performs an operation to avoid the obstacle. Therefore, when the information processing apparatus further acquires the above-described travel state (predetermined travel information), it is estimated that there is an obstacle, so that a more accurate estimation can be performed. Further, when the information processing apparatus further uses the vehicle travel information to estimate the presence of an obstacle, the dependence on the image analysis process using only the image information can be reduced, and the processing burden can be reduced.

[0086] (Aspect 7) An information processing apparatus according to one aspect may include a third estimation unit that estimates a disturbance in traffic flow based on the travel state of the vehicle acquired by the second acquisition unit. When there is an obstacle on the road, it is considered that the traffic flow of the vehicle is disturbed in order to avoid the obstacle. At that time, the vehicle is considered to be in the above-described predetermined travel state. Therefore, the information processing apparatus can estimate the disturbance in traffic flow based on the travel state of the vehicle. By estimating the disturbance in traffic flow, the information processing apparatus can provide driving support for both a manned vehicle and an unmanned vehicle.

[0087] (Aspect 8) In an information processing apparatus according to one aspect, the third estimation unit may estimate that there is a disturbance in the traffic flow when the travel state of the vehicle acquired by the second acquisition unit has a change amount or an operation as a threshold value or more. The driving operation to avoid obstacles is considered to result in a larger change in the amount of driving operation and the driving state compared to, for example, when the vehicle is traveling along the road (in the proper lane). Therefore, when there is a change amount or a driving state exceeding the threshold for the above-described predetermined driving state, the information processing device can estimate that there is a disruption in the traffic flow.

[0088] (Aspect 9) An information processing device according to one aspect includes a third acquisition unit that acquires facility information regarding the position and business hours of a facility. The first acquisition unit acquires vehicle information including position information regarding the traveling position of the vehicle. The third estimation unit may estimate that there is a disruption in the traffic flow when it is estimated that there is an operating facility within a predetermined distance from the traveling position of the vehicle based on the position information acquired by the first acquisition unit and the facility information acquired by the third acquisition unit. For example, when a facility is in operation, it is considered that vehicles will park in a relatively close range from that facility. In such a case, there is a possibility that the traffic flow will be disrupted to avoid parked vehicles. Therefore, the information processing device can estimate whether there is a disruption in the traffic flow according to the operating state of the facility.

[0089] (Aspect 10) An information processing device according to one aspect may include a storage unit that stores map information regarding a road map, and a registration unit that registers the position based on the estimation in at least one of the case where the presence of an obstacle is estimated by the second estimation unit and the case where it is estimated that there is a disruption in the traffic flow by the third estimation unit in the map information. The information processing device can register a position where the vehicle has difficulty traveling in the road map (map information). By providing the user (for example, a driver, etc.) with the road map (map information), the information processing device can improve the convenience for the user.

[0090] (Aspect 11) An information processing apparatus according to one aspect may include a route guidance unit that performs route guidance from a departure point to an arrival point by using a road map in which at least one of a position where an obstacle is estimated to exist and a position where traffic flow is disrupted is registered. The information processing apparatus can perform route guidance using a road that is easier for the vehicle to travel on. As a result, the information processing apparatus can reduce the burden on the driver's driving and suppress the occurrence of accidents and the like.

[0091] (Aspect 12) An information processing apparatus according to one aspect may include a route guidance unit that performs guidance when traveling on a road by using a road map in which at least one of a position where an obstacle is estimated to exist and a position where traffic flow is disrupted is registered. Thereby, even when the vehicle travels along the road during autonomous driving, for example, the information processing apparatus uses the road map of the planned route to estimate whether there is an obstacle on the planned route, that is, whether there is a position where the traffic flow is disrupted on that route. When there is an obstacle (when there is a disruption in the traffic flow), avoidance measures such as lane changes can be taken in advance.

[0092] (Aspect 13) In an information processing method according to one aspect, a computer executes a first acquisition step of acquiring vehicle information including image information regarding an image generated by an in-vehicle camera, a first estimation step of estimating lanes assigned to a road based on the image information acquired in the first acquisition step, and a second estimation step of estimating whether there is an obstacle between the lanes estimated in the first estimation step and a predetermined distance in the road width direction. Thereby, the information processing method can achieve the same effects as the information processing apparatus according to the above-described one aspect.

[0093] (Aspect 14) An information processing program according to one aspect causes a computer to implement a first acquisition function of acquiring vehicle information including image information regarding an image generated by an in-vehicle camera, a first estimation function of estimating lanes marked on a road based on the image information acquired by the first acquisition function, and a second estimation function of estimating whether there is an obstacle within a predetermined distance in the road width direction from the lanes estimated by the first estimation function. Thereby, the information processing program can achieve the same effects as the information processing apparatus according to the above-described one aspect.

Explanation of Signs

[0094] 1 Information processing system 10 Vehicle 20 Server 30 Information processing apparatus 311 Control unit 312 First acquisition unit 313 Second acquisition unit 314 Third acquisition unit 315 First estimation unit 316 Second estimation unit 317 Third estimation unit 318 Registration unit 319 Route guidance unit 320 Output control unit 331 Communication unit 332 Storage unit 333 Display unit

Claims

1. A storage unit that stores map information related to a road map, A first acquisition unit that acquires vehicle information including image information related to an image generated by an in-vehicle camera and position information related to the traveling position of the vehicle, Based on the image information acquired by the first acquisition unit, it estimates the lane attached to the road. As the lane, at least one of the lane boundary line between adjacent traffic lanes of a road with a plurality of traffic lanes on one side and the center line of the lane on the most oncoming lane side of a road with one or more traffic lanes on one side, and a first estimation unit that estimates at least one of the outermost lane lines on the shoulder side of a road with one or more traffic lanes on one side, It estimates whether there is an obstacle between the lane estimated by the first estimation unit and a predetermined distance in the road width direction. Based on the position information acquired by the first acquisition unit and the map information stored in the storage unit, it does not estimate the presence of an obstacle within an intersection, and a second estimation unit that estimates the presence of an obstacle when the outermost lane line cannot be recognized, An information processing apparatus comprising:

2. The second estimation unit estimates whether there is an obstacle based on the image information acquired by the first acquisition unit The information processing apparatus according to claim 1.

3. The first acquisition unit acquires vehicle information including feature recognition information related to features recognized based on the measurement results of in-vehicle sensors, The second estimation unit estimates whether there is an obstacle based on the feature recognition information acquired by the first acquisition unit The information processing apparatus according to claim 1 or 2.

4. The first estimation unit can estimate at least one of a lane boundary line and a center line of a lane as the lane, The second estimation unit estimates whether there is an obstacle between the lane boundary line or the center line of the lane and a predetermined distance in the road width direction, The information processing apparatus according to any one of claims 1 to 3.

5. The first acquisition unit acquires vehicle information including vehicle driving information related to the driving of the vehicle, based on the vehicle driving information acquired by the first acquisition unit, when the steering angle toward the center of the road changes while the vehicle is driving, when there is an operation of the turn signal toward the center of the road while the vehicle is driving, when there is a change in which the driving speed of the vehicle decreases, and when there is a change in which the inter-vehicle distance with a vehicle driving ahead or behind becomes narrow, a second acquisition unit that acquires the driving state of at least one vehicle among these cases, and the second estimation unit estimates the presence of an obstacle based on the driving state of the vehicle acquired by the second acquisition unit The information processing apparatus according to any one of claims 1 to 4.

6. It includes a third estimation unit that estimates the disruption of the traffic flow based on the driving state of the vehicle acquired by the second acquisition unit The information processing apparatus according to claim 5.

7. When the driving state of the vehicle acquired by the second acquisition unit has a change amount or an operation as a threshold value or more, the third estimation unit estimates that there is a disruption in the traffic flow The information processing apparatus according to claim 6.

8. It includes a third acquisition unit that acquires facility information regarding the location and business hours of a facility, When it is estimated based on the position information acquired by the first acquisition unit and the facility information acquired by the third acquisition unit that there is an operating facility within a predetermined distance from the driving position of the vehicle, the third estimation unit estimates that there is a disruption in the traffic flow The information processing apparatus according to claim 6 or 7.

9. a storage unit that stores map information regarding a road map, a registration unit that registers the position based on the estimation in at least one of the case where the presence of an obstacle is estimated by the second estimation unit and the case where it is estimated by the third estimation unit that there is a disruption in the traffic flow in the map information The information processing apparatus according to any one of claims 6 to 8, comprising

10. A route guidance unit that performs route guidance from a departure point to an arrival point using the map information regarding a road map in which at least one of a position where an obstacle is estimated to exist and a position where traffic flow is disturbed is registered. The information processing apparatus according to claim 9.

11. A route guidance unit that performs guidance when driving on a road using the map information regarding a road map in which at least one of a position where an obstacle is estimated to exist and a position where traffic flow is disturbed is registered. The information processing apparatus according to claim 9.

12. A computer comprising a storage unit that stores map information regarding a road map, A first acquisition step of acquiring vehicle information including image information regarding an image generated by an in-vehicle camera and position information regarding the traveling position of the vehicle, Based on the image information acquired in the first acquisition step, it estimates the lanes assigned to the road. As the lanes, at least one of the lane boundary lines between adjacent traffic lanes of a road with a plurality of traffic lanes on one side and the center line of the lane on the most oncoming lane side of a road with one or more traffic lanes on one side, and the outermost lane line on the shoulder side of a road with one or more traffic lanes on one side is estimated. A first estimation step, It is estimated whether there is an obstacle between the lanes estimated in the first estimation step and a predetermined distance in the road width direction. Based on the position information acquired in the first acquisition step and the map information stored in the storage unit, the presence of an obstacle is not estimated within an intersection, and it is estimated that there is an obstacle when the outermost lane line cannot be recognized. A second estimation step, An information processing method for executing.

13. In a computer comprising a storage unit that stores map information regarding a road map, A first acquisition function that acquires vehicle information including image information regarding an image generated by an in-vehicle camera and position information regarding the traveling position of the vehicle; Based on the image information acquired by the first acquisition function, it estimates the lanes assigned to the road. As the lanes, it estimates at least one of the lane boundary lines between the adjacent traffic lanes of a road with a plurality of traffic lanes on one side and the center line of the lane on the most oncoming lane side of a road with one or more traffic lanes on one side, and the outermost lane line on the shoulder side of a road with one or more traffic lanes on one side. A first estimation function; It estimates whether there is an obstacle between the lanes estimated by the first estimation function and a predetermined distance in the road width direction. Based on the position information acquired by the first acquisition function and the map information stored in the storage unit, it does not estimate the presence of an obstacle within an intersection, and estimates that there is an obstacle when the outermost lane line cannot be recognized. A second estimation function; An information processing program for realizing the above.

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