JP7693568B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

This disclosure relates to an information processing device, an information processing method, and an information processing program.

For example, if a vehicle driver encounters an obstacle such as a parked vehicle ahead in the lane in which the vehicle is traveling, the driver may need to perform driving maneuvers to avoid the obstacle. In this case, if the driver knows in advance that an obstacle exists, he or she may be able to perform driving maneuvers with ample time to maneuver, potentially preventing an accident from occurring.

Patent Document 1 discloses a device that supports the driving of a vehicle by recognizing obstacles blocking the lane ahead in which the vehicle is driving. The device acquires point cloud data of the surrounding environment using a LiDAR mounted on the vehicle and recognizes the presence of obstacles. In this case, the device recognizes areas where the lane is blocked by parked vehicles that are an obstacle to the driving of the vehicle.

JP 2021-009655 A

As described above, the device disclosed in Patent Document 1 recognizes obstacles by obtaining point cloud data using a LIDAR. However, LIDAR is not installed in many vehicles. Vehicles that are not equipped with a LIDAR cannot recognize obstacles.
For this reason, it is desirable to estimate the presence of an obstacle by utilizing devices installed in as many vehicles as possible.

The present disclosure provides an information processing device, an information processing method, and an information processing program capable of estimating the presence of an obstacle.

An information processing device according to one embodiment includes a first acquisition unit that acquires vehicle information including image information related to an image generated by an on-board camera, a first estimation unit that estimates lanes on a road based on the image information acquired by the first acquisition unit, and a second estimation unit that estimates whether an obstacle is present within a predetermined distance in the road width direction from the lane estimated by the first estimation unit.

According to one embodiment, image information about an image generated by an onboard camera can be used to estimate whether an obstacle is present in the vehicle's direction of travel.

FIG. 1 is a diagram illustrating an information processing system according to an embodiment. FIG. 1 is a block diagram illustrating an information processing device according to an embodiment. FIG. 1 is a diagram for explaining an example of a road with one lane on each side. FIG. 1 is a diagram for explaining an example of a road with three lanes on each side. 1 is a flowchart illustrating an information processing method according to an embodiment.

One embodiment is described below.

[Overview of information processing system 1]
First, an overview of an information processing system 1 according to an embodiment will be described.
FIG. 1 is a diagram illustrating an information processing system 1 according to an embodiment.

The information processing system 1 includes, for example, a vehicle 10, a server 20, and an information processing device 30.
The information processing device 30 may be configured, for example, as an obstacle estimation device that estimates whether an obstacle exists in the traveling direction of the vehicle 10. The information processing device 30 may also be configured, for example, as a traffic flow estimation device that estimates whether a traffic flow disturbance will occur in the traveling direction of the vehicle 10. The information processing device 30 may also be configured, for example, as a map registration device that registers the above-mentioned estimation result 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.

The information processing device 30 acquires vehicle information generated by the vehicle 10. As an example of this case, the information processing device 30 may acquire the vehicle information from at least one selected from the server 20 and the group of the vehicle 10. Note that here, when the vehicle 10 generates the vehicle information, the vehicle information may be transmitted to the server 20 or the information processing device 30, for example.
The vehicle information may include, for example, image information generated by capturing an image of the traveling direction by an imaging unit (on-board camera) mounted on the vehicle 10 .
The vehicle information may also include information regarding the results of recognition of objects around the vehicle by a sensor (on-board sensor) mounted on the vehicle 10. In this case, as an example, the object may be a feature on the ground, and the information may be feature recognition information. As another example, the on-board sensor may recognize the object (feature on the ground) by using laser light.
The vehicle information may also include, for example, vehicle driving information related to the driving of the vehicle 10. The vehicle driving information may be, for example, probe information, CAN information, and the like.
Furthermore, the vehicle information may include, for example, position information regarding the traveling position of the vehicle 10. As an example in this case, the vehicle 10 may acquire the position information using GNSS or the like.
The vehicle information may also include, for example, time information relating to the time.

The information processing device 30 uses image information to estimate the lanes of the road on which the vehicle 10 is traveling. The lanes may be, for example, the center line of the road if the road has one lane on each side, or the center line or lane boundary line if the road has two or more lanes on each side. Here, the lane may be, for example, the lane boundary line of the first lane when the road has two or more lanes on each side.

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

[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 an information processing device 30 according to an embodiment.

The information processing device 30 includes, for example, a communication unit 331, a memory unit 332, a display unit 333, and a control unit 311. The communication unit 331, the memory 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, for example, by an arithmetic processing device of the information processing device 30. The control unit 311 (e.g., a calculation processing unit, etc.) may realize the functions of each unit (e.g., 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, the output control unit 320, etc.) by, for example, appropriately reading and executing various programs, etc. stored in the memory unit 332, etc.

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, a server 20 and a vehicle 10.

The storage unit 332 may store, for example, various information and programs. Examples of the storage unit 332 may be a memory, a solid state drive, a hard disk drive, etc. The storage unit 332 may be, for example, a storage area on a cloud, a server, etc. The storage unit 332 may also store map information related to a road map. The map information may include, for example, location information such as longitude and latitude.

The display unit 333 is capable of displaying, for example, various characters, symbols, images, etc.

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

The first acquisition unit 312 may acquire vehicle information including feature recognition information regarding features recognized based on the measurement results of an on-board sensor. The on-board sensor may, for example, recognize features by emitting laser light and receiving laser light (reflected light) reflected by the features. An example of an on-board sensor may be a sensor that generates point cloud data.

The first acquisition unit 312 may acquire vehicle information including vehicle driving information related to the driving of the vehicle 10. The vehicle driving information may be, for example, information related to the driving operation of the vehicle 10, or information acquired by various sensors and devices mounted on the vehicle 10.

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.

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 by the first acquisition unit 312.
(1) When the steering angle changes toward the center of the road while the vehicle 10 is traveling. (2) When the turn signal is operated toward the center of the road while the vehicle 10 is traveling. (3) When the traveling speed of the vehicle 10 changes to slow down. (4) When the distance between the vehicle 10 traveling ahead or behind the vehicle 10 changes to narrow.

Regarding (1) above, the change in steering angle toward the center of the road when the vehicle 10 is traveling may be, for example, a change in 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 on which the vehicle 10 is regulated to drive on the left side (for example, a road in Japan, etc.), the change in steering angle may be toward the right. In this case, for example, the steering angle of the vehicle 10 is different when the vehicle 10 changes lanes to the right and when the vehicle 10 turns right. 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 predetermined angle range. For this reason, for example, it is possible to predefine the range of the steering angle when the vehicle 10 changes lanes.

Regarding (2) above: 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 (for example, operation of the turn signal toward the right).

Regarding (3) above, the change in the traveling speed of the vehicle 10 to slow down can be obtained, for example, from the rotational speed of the tire axle or drive shaft, the speed indicated by a speedometer mounted on the vehicle 10, and whether or not the driver operates the brakes, etc.

Regarding (4) above, the change in the distance to the vehicle 10 traveling ahead or behind the vehicle 10 to become narrower can be obtained by, for example, continuously or intermittently monitoring the distance to the preceding vehicle or the following vehicle over time using an on-board sensor (such as an obstacle sensor, for example) that monitors the front and rear of the vehicle 10. Here, the on-board sensor may be mounted on the vehicle itself to obtain the distance to the preceding vehicle or the following vehicle, may be mounted on the preceding vehicle to obtain the distance to the following vehicle, or may be mounted on the following vehicle to obtain the distance to the preceding vehicle.
The above-described first acquisition unit 312 may acquire vehicle driving information (vehicle information) including a vehicle distance acquired by the vehicle itself, the preceding vehicle, or the following vehicle from each vehicle 10. Alternatively, the first acquisition unit 312 may acquire vehicle driving information (vehicle information) including a vehicle distance acquired by the preceding vehicle or the following vehicle via the vehicle itself.

The third acquisition unit 314 may acquire facility information related to the location and business hours of the facility. The facility may be, for example, a variety of facilities including restaurants and stores that sell products or provide services. The location of the facility may be, for example, a location based on the longitude and latitude of the facility's location. The facility information may be, for example, registered in map information or may be stored in the server 20 or the like. The third acquisition unit 314 may also acquire, for example, the location and business hours of the facility published on a communication network as facility information.

The first estimation unit 315 estimates the lanes of the road based on the image information acquired by the first acquisition unit 312. The first estimation unit 315 may estimate the lanes of the road recorded in the image information, for example, based on a learned model that has learned the shape and color, etc., of the lanes and the like. Alternatively, the first estimation unit 315 may estimate the lanes of the road recorded in the image information, for example, by using pattern matching based on a pattern of the shape, etc., of the lanes.

In this case, the first estimation unit 315 may be capable of estimating at least one of a lane boundary line and a roadway centerline as the lane. For example, the lane may be the roadway centerline 401 when the road has one lane on each side (see FIG. 3), and may be the roadway centerline 401 or the lane boundary line 402 when the road has two or more lanes on each side (see FIG. 4). Here, for example, the lane boundary line 402 when the road has two or more lanes on each side may be the lane boundary line 402a of the first lane 411, etc.
Furthermore, the first estimation unit 315 may estimate the outer roadway 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 roadway centerline 401, and the outer roadway line 403 as the lane.

The second estimation unit 316 estimates whether an obstacle exists within a predetermined distance in the road width direction from the lane estimated by the first estimation unit 315. In this case, the second estimation unit 316 may estimate whether an obstacle exists within a predetermined distance in the road width direction from the lane boundary line or the center line of the road. In other words, the second estimation unit 316 may estimate, for example, whether an obstacle exists within a predetermined distance in the direction of the outer side line of the road (leftward on Japanese roads) from the center line or lane boundary line of the road as the lane estimated by the first estimation unit 315.

FIG. 3 is a diagram for explaining an example of a road with one lane in each direction.
FIG. 4 is a diagram for explaining an example of a road with three lanes on each side.

For example, when the lane estimated by the first estimation unit 315 is the center line 401 of a road with one lane in each direction (see FIG. 3), or the lane boundary line 402a of the first lane 411 of a road with two or more lanes in each direction (see FIG. 4), the second estimation unit 316 may estimate whether an obstacle 421 is present within a predetermined distance L1, L2 from the center line 401 or the lane boundary line 402a in the direction of the outer lane line 403. As a specific example in this case, the predetermined distances L1, L2 may be 3.5 m, or may be various other distances. Here, 3.5 m is an example, and may be set appropriately taking into account a width that is easy for a vehicle to travel through.

In addition, for example, when the lane estimated by the first estimation unit 315 is a lane of a road with two or more lanes on each side, the second estimation unit 316 may set the total distance obtained by adding a distance that includes the second lane 412 and the lane width of one or more lanes on the side of the center line 401 of the roadway from the second lane 412 to the above-mentioned specified distance as the specified distance L3, L4, and estimate whether there is an obstacle 421 between the lane on the side of the center line 401 of the roadway from the second lane 412 (lane boundary line 402b and center line 401 of the roadway) and the specified distance (see Figure 4).
As a specific example, when the road estimated by the first estimation unit 315 is a road with two lanes on each side, the second estimation unit 316 may set the total distance from the center line 401 of the roadway to the lane width of the second lane 412 plus the above-mentioned example of 3.5 m, and estimate whether an obstacle 421 is present between the center line 401 and the specified distance.
As another specific example, when the road estimated by the first estimation unit 315 is a road with three lanes on each side, the second estimation unit 316 may set the total distance from the lane boundary line 402b between the second lane 412 and the third lane 413 to the lane width of the second lane 412 plus the above-mentioned example of 3.5 m as a predetermined distance L3, and estimate whether an obstacle 421 is present between the lane boundary line 402b and the predetermined distance L3 (see Figure 4).
As another specific example, when the road estimated by the first estimation unit 315 is a road with three lanes on each side, the second estimation unit 316 may set the total distance from the center line 401 of the roadway to the specified distance L4, which is the sum of the lane widths of the second lane 412 and the third lane 413 and the above-mentioned example of 3.5 m (see Figure 4), and estimate whether an obstacle 421 is present between the center line 401 of the roadway and the specified distance L4 (see Figure 4).

The obstacle may be, for example, a parked vehicle, a feature, or the like, which may impede vehicle 10 when traveling along a road. The obstacle when traveling vehicle 10 may be, for example, a steering operation such as changing lanes, or a driving operation such as slowing down, which requires an operation to avoid the obstacle.

The second estimation unit 316 may estimate whether an obstacle is present based on the image information acquired by the first acquisition unit 312. The second estimation unit 316 may estimate whether an obstacle is recorded in the image information based on, for example, a trained model that has learned the shape of a vehicle and patterns of various obstacles such as poles and trees and other features, and the image information. Alternatively, the second estimation unit 316 may estimate whether an obstacle is recorded in the image information by using pattern matching based on the shape of a lane and patterns of features. In this case, for example, when the image information is information in which a moving image is recorded and information in which still images are recorded consecutively at a predetermined time interval, the second estimation unit 316 can estimate whether an object recorded in the image information is stationary or moving by using frames and still images that are different in time. The second estimation unit 316 can estimate a stationary object as an obstacle by using a trained model and pattern matching.

The second estimation unit 316 may estimate whether an obstacle exists based on the feature recognition information acquired by the first acquisition unit 312. That is, when a feature is recognized based on the detection result of an on-board sensor, for example, the second estimation unit 316 may estimate whether an obstacle exists based on the recognition result (feature recognition information).

The second estimation unit 316 may estimate that an obstacle is present when the outer roadway line cannot be recognized. That is, for example, when the outer roadway line is estimated by the first estimation unit 315, the second estimation unit 316 may estimate that an obstacle is present when the outer roadway line cannot be recognized in the image information. In this case, the second estimation unit 316 may estimate that an obstacle is present, for example, when frames and still images (images) from different times are used, and the outer roadway line can be recognized in an image at a certain time t, but the outer roadway line cannot be recognized in an image within a predetermined time after the time t. Furthermore, the second estimation unit 316 may estimate the presence of an obstacle, for example, when the first estimation unit 315 was able to recognize the outer lane line based on multiple vehicle information from the same position (approximately the same position), but the outer lane line becomes unrecognizable within a predetermined time from the time when the outer lane line was last recognized, and then becomes recognizable within a predetermined time thereafter, that is, when the first estimation unit 315 is temporarily unable to recognize the outer lane line.

Alternatively, the second estimation unit 316 may estimate that an obstacle exists when the outer lane line cannot be recognized at the same position (same area) during a specific period of time based on multiple image information, for example.
In addition, the second estimation unit 316 may estimate that the outer roadway line has become unrecognizable due to wear or removal, etc., when the outer roadway line cannot be recognized at the same position (same area) for a relatively long period of time based on multiple image information, for example.

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

The second estimation unit 316 may estimate the presence of an obstacle 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, for example, 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 acquire, for example, weather information related to the weather when the vehicle 10 generating the vehicle information was traveling from the server 20 or the like. The second estimation unit 316 may specify the time when the obstacle exists based on, for example, time information (vehicle information). In this case, the second estimation unit 316 may, for example, tally up the estimation results for each predetermined period, and specify an area in which the presence of multiple obstacles is estimated (an area including positions in which the presence of a predetermined number or more obstacles is estimated) based on the day of the week, the time period, the weather, and the business status of nearby facilities. 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 into units of 100 m or the like of the road. The second estimation unit 316 may, for example, identify the position where the presence of an obstacle is estimated based on vehicle information acquired in real time and vehicle information acquired in the past.

In addition, in sections of roads where there are traffic regulations such as parking bans according to days of the week and time periods, it is possible that vehicles will be parked outside of the traffic regulation periods. In this case, when estimating the presence of an obstacle according to the above-mentioned days of the week and time periods, the second estimation unit 316 can also estimate the presence or absence of traffic regulations such as parking bans according to the days of the week and time periods.

In addition, the second estimation unit 316 does not need to estimate the presence of an obstacle within an intersection based on the position information (vehicle information) and map information.

The third estimation unit 317 may estimate a traffic disturbance based on the traveling state of the vehicle 10 acquired by the second acquisition unit 313. That is, the third estimation unit 317 may estimate that there is a traffic disturbance when the second acquisition unit 313 acquires the traveling state of at least one vehicle 10 selected from the above groups (1) to (4). Here, the traffic flow may be, for example, the overall flow of multiple vehicles, etc., which is an accumulation of the movements of multiple vehicles, etc. on a road. The traffic disturbance may be at least one of the following cases: when the traffic volume is relatively high, when the traffic density is relatively high, and when the traveling speed is relatively slow, unlike when multiple vehicles, etc. are running smoothly on a road.

The third estimation unit 317 may estimate that there is a disturbance in traffic flow when the driving state of the vehicle 10 acquired by the second acquisition unit 313 includes a driving state with a change amount or operation equal to or greater than a threshold value.
For example, when the second acquisition unit 313 acquires (1) the running state of the vehicle 10 in which the steering angle changes toward the center of the road when the vehicle 10 runs (the change in steering angle when the vehicle 10 changes lanes), the third estimation unit 317 may estimate that there is a disturbance in the traffic flow if there is a change in steering angle that is greater than the steering angle of the steering wheel when the vehicle 10 runs along the road (when running along the road). Here, for example, the steering angle when the vehicle 10 changes lanes is considered to be greater than the steering angle when the vehicle 10 runs along the road. For this reason, the upper limit of the change in steering angle when the vehicle 10 runs along the road may be set as the threshold A. Also, for example, the steering angle of the vehicle 10 when the vehicle 10 turns right (or left) is considered to be greater than the steering angle when the vehicle 10 changes lanes. For this reason, an upper limit threshold B (threshold A<threshold B) may also be set for the steering angle when the vehicle 10 changes lanes, and the range between the two thresholds A and B may be the change in steering angle when the vehicle 10 changes lanes. Here, the thresholds A and B may be set in advance.

In addition, when the second acquisition unit 313 acquires (2) the driving state of the vehicle 10, such as the operation of the turn signal toward the center of the road while the vehicle 10 is traveling (the operation of the turn signal when the vehicle 10 changes lanes), the third estimation unit 317 may estimate that there is a disruption in traffic flow based on the operation of the turn signal (turn signal on).

In addition, when the third estimation unit 317 acquires the driving state of the vehicle 10, for example, (3) a change in which the driving speed of the vehicle 10 slows down, using the second acquisition unit 313 as a reference, the third estimation unit 317 may estimate that there is a disturbance in the traffic flow when the driving speed slows down to a threshold value or less based on the driving speed just before the deceleration. Here, the threshold value may be set in advance.

In addition, when the second acquisition unit 313 acquires (4) the traveling state of the vehicle 10 in which the distance between the vehicle 10 traveling ahead or behind the vehicle 10 is narrowed, the third estimation unit 317 may estimate that there is a disturbance in the traffic flow when the distance between the vehicles 10 becomes shorter than or equal to a threshold value based on the distance between the vehicles before the change in which the distance between the vehicles becomes shorter. Here, the threshold value may be set in advance.

The third estimation unit 317 may estimate that there is a disturbance in the traffic flow based on at least one of the driving conditions (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, one of the following (A) to (C) is met.
(A) When one of the driving conditions (1) to (4) above is acquired; (B) When two (or three) of the driving conditions (1) to (4) above are acquired; (C) When all of the driving conditions (1) to (4) above are acquired.

The third estimation unit 317 may estimate that there is a disturbance in traffic flow when it is estimated that there is a facility in business 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 identify a position where 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). The third estimation unit 317 may also identify whether a facility is located within a predetermined distance from the identified position based on, for example, the identified position and facility information (position of the facility). Furthermore, the third estimation unit 317 may estimate whether the facility corresponding to the identified location is in business based on, for example, time information (facility information) and facility information (business hours of the facility). The third estimation unit 317 may estimate that there is a disturbance in traffic flow when it is estimated that the facility corresponding to the identified location is in business.

The third estimation unit 317 may, for example, obtain weather information on the weather when the vehicle 10 that generated the vehicle information was traveling from the server 20 or the like. The third estimation unit 317 may, for example, identify the time when there is traffic disruption based on time information (vehicle information). In this case, the third estimation unit 317 may, for example, tally up the estimation results for each predetermined period and identify areas where multiple traffic disruption is estimated to exist (areas including positions where a predetermined number or more traffic disruption is estimated to exist) based on the day of the week, the time period, the weather, and the business status of nearby facilities. The third estimation unit 317 may, for example, identify the position where there is traffic disruption for each link unit of the road network, or for each section unit divided into units of 100 m or the like of the road. The third estimation unit 317 may, for example, identify the position where the existence of an obstacle is estimated based on vehicle information acquired in real time and vehicle information acquired in the past.

In addition, when the third estimation unit 317 estimates traffic flow disruptions according to the day of the week and time of day described above, for example, 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 of day.
Furthermore, the third estimation unit 317 does not need to estimate the presence of an obstacle within an intersection based on the position information (vehicle information) and map information.

The registration unit 318 may register a position based on the estimation in the map information when at least one of the cases where the second estimation unit 316 estimates the presence of an obstacle and the third estimation unit 317 estimates that there is a traffic flow disruption. That is, the registration unit 318 may, for example, register a position where the presence of an obstacle is estimated in a road map (map information). The registration unit 318 may also register, for example, a position where there is an estimated traffic flow disruption in a road map (map information).

The registration unit 318 may rank the ease of driving the vehicle 10 based on the number of cases where the presence of obstacles is estimated and the number of cases where traffic flow is estimated to be disrupted, and may register the ranking in the road map (map information) in association with the estimated location. For example, if the number of cases is relatively large, the registration unit 318 may rank the vehicle 10 as being more difficult to drive. Also, for example, if the number of cases is relatively small, the registration unit 318 may rank the vehicle 10 as being more easy to drive.

In this case, the registration unit 318 may rank the ease of driving the vehicle 10 according to the number of lanes (number of lanes on one side) on the road at the position where the presence of the obstacle is estimated and at the position where the traffic flow is estimated to be disrupted, and may register the ranking in the road map (map information) in association with the estimated position. For example, when the number of lanes described above is relatively small, the registration unit 318 may rank the vehicle 10 as being more difficult to drive on. Also, for example, when the number of lanes described above is relatively large, the registration unit 318 may rank the vehicle 10 as being more easy to drive on.

The registration unit 318 may also rank the ease of driving the vehicle 10 according to the width of the road (e.g., the width of the first lane) at the position where the presence of an obstacle is estimated and at the position where the traffic flow is estimated to be disrupted, and register the ranking in the road map (map information) in association with the estimated position. For example, if the road width described above is relatively narrow, the registration unit 318 may rank the road as being more difficult for the vehicle 10 to drive on. For example, if the road width described above is relatively wide, the registration unit 318 may rank the road as being more easy for the vehicle 10 to drive on.

The registration unit 318 may also register the results of estimation (presence of obstacles and traffic disruptions) according to the day of the week, time period, weather, and facility business status in the road map (map information). This allows the registration unit 318 to provide information to a user (e.g., a driver, etc.) who uses the road map (map information) as to whether it is consistently difficult to drive the vehicle 10 or whether it is temporarily difficult to drive the vehicle 10.

The route guidance unit 319 may provide route guidance from the departure point to the destination using a road map in which at least one of the positions where the presence of an obstacle is estimated and the positions where traffic flow is disrupted is registered. The route guidance unit 319 may be, for example, a navigation function. For example, when the route guidance unit 319 receives a request for route guidance from the departure point to the destination (route guidance request), the route guidance unit 319 may search for a detour route that avoids the positions where the presence of an obstacle is estimated and the positions where traffic flow is disrupted, and provide route guidance using the detour route. In addition, when the route guidance unit 319 receives a route guidance request, for example, the route guidance unit 319 may provide route guidance such as traveling in lanes that avoid the positions where the presence of an obstacle is estimated and the positions where traffic flow is disrupted (for example, in the case of a road with two or more lanes in each direction, other lanes except the first lane). In addition, for example, when the registration unit 318 registers a ranking of the ease of driving of the vehicle 10 in a road map (map information), the route guidance unit 319 may provide route guidance such that the vehicle 10 travels along a road (or a lane) for which a ranking that makes it easier to drive 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 a user of the information processing device 30. In this case, the user terminal may be, for example, a portable terminal (for example, a laptop, a tablet, a smartphone, etc.). The route guidance unit 319 may output information related to route guidance to the user terminal via the communication unit 331, for example.

For example, when vehicle information is acquired in real time, the route guidance unit 319 may acquire information on the ease or difficulty of driving 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 information obtained from the current situation information, without performing statistical estimation processing using the vehicle driving information. As an example, the route guidance unit 319 may acquire a guide route based on the use of statistical estimation processing in a route search when the vehicle 10 departs, and perform route guidance after the vehicle 10 departs that takes into account the above-mentioned estimation processing in almost real time. That is, for example, in route guidance after departure, the route guidance unit 319 may guide the vehicle to a lane that is easy to drive on based on estimation. Furthermore, for example, in route guidance after departure, the route guidance unit 319 may acquire a detection result of a sensor (on-board sensor) mounted on the vehicle, and guide the vehicle to a lane that is easy to drive on according to the estimation result in almost real time based on the result.

For example, when vehicle information is acquired in real time, the route guidance unit 319 may provide route guidance using the estimation results registered in the road map by the registration unit 318 in almost real time. In this case, the registration unit 318 may, for example, register the degree of ease of driving for the vehicle 10 in the road map (map information) in relation to the link cost of the route search. The route guidance unit 319 may, for example, present guidance route candidates that prioritize the degree of ease of driving for the vehicle 10.

The route guidance unit 319 can also provide route guidance for both manned and unmanned vehicles. In this case, for example, when the vehicle 10 is set to an autonomous driving mode, a driving assistance mode, or the like, the route guidance unit 319 may change the guidance route according to the degree of ease of driving ranked in the road map (map information) by the registration unit 318. Also, when the vehicle 10 is set to an autonomous driving mode, a driving assistance mode, or the like, the route guidance unit 319 may select each control mode, such as the autonomous driving mode or the driving assistance mode, or may switch between those control modes according to the degree of ease of driving ranked in the road map (map information) by the registration unit 318.

The route guidance unit 319 may also provide guidance when traveling on a road, for example, using a road map in which at least one of the positions where the presence of an obstacle is estimated and the positions where traffic flow is disrupted is registered. For example, when the vehicle 10 travels along a road, the route guidance unit 319 may use a road map of the planned route (road) to estimate whether there is an obstacle on the route. That is, the route guidance unit 319 may use a road map to estimate whether there is a traffic flow disruption on the planned route. For example, when it is estimated that there is an obstacle (traffic flow disruption) on the planned route (road), the route guidance unit 319 may present a lane that is easy to travel on. That is, the route guidance unit 319 may provide guidance to take an avoidance measure such as changing lanes in advance before reaching a position where an obstacle is located (a position where traffic flow is disrupted). The route guidance unit 319 may provide the above-mentioned guidance, for example, whether the vehicle 10 is a manned vehicle or an unmanned vehicle. As an example, the route guidance unit 319 may control the vehicle's driving so that avoidance measures such as lane changes are taken in advance, or may provide driving assistance for the vehicle's driving.

The route guidance unit 319 may provide route guidance using, for example, characters, symbols, images, and audio.

The output control unit 320 may control the output unit to output information indicating that the second estimating unit 316 has estimated the presence of an obstacle and its estimated position.
The output control unit 320 may control the output unit to output the fact that the third estimating unit 317 has estimated that there is a traffic flow disturbance and its estimated location.
The output control unit 320 may control the output unit to output a road map whose position is registered by the registration unit 318 .
Here, the output unit may be a communication unit 331, a storage unit 332, a display unit 333, and the like.

That is, the output control unit 320 may control the communication unit 331 to transmit, for example, the above-mentioned estimation result and position, and at least one selected from the group of 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), etc. In this case, the user terminal may be, for example, a desktop, a laptop, a tablet, a smartphone, etc.
Furthermore, the output control unit 320 may control the storage unit 332 to store, for example, at least one selected from the group of the above-mentioned estimation result and position, and a road map.
Furthermore, the output control unit 320 may control the display unit 333 to display at least one selected from the group of the above-mentioned estimation results and positions, and a road map, for example.

[Information processing method]
Next, an information processing method according to an embodiment will be described.
FIG. 5 is a flowchart illustrating an information processing method according to an embodiment.

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

In step ST102, the first estimation unit 315 estimates lanes on the road based on the image information acquired in step ST101. In this case, the first estimation unit 315 may be capable of estimating at least one of a lane boundary line and a roadway centerline as the lane. The first estimation unit 315 may also estimate at least one of a lane boundary line and a roadway centerline, and an outer roadway line as the lane.

In step ST103, the second estimation unit 316 estimates whether an obstacle exists within a predetermined distance in the road width direction from the lane (e.g., lane boundary line or roadway center line, etc.) estimated in step ST102. In this case, the second estimation unit 316 may estimate whether an obstacle exists based on the image information acquired in step ST101. The second estimation unit 316 may also estimate whether an obstacle exists based on the feature recognition information in step ST101. The second estimation unit 316 may estimate that an obstacle exists when the outer roadway line cannot be recognized.

Here, the second acquisition unit 313 may acquire at least one of the following driving conditions of the vehicle 10 in the cases (1) to (4) based on the vehicle driving information acquired in step ST101.
(1) When the steering angle changes toward the center of the road while the vehicle 10 is traveling (2) When the turn signal is operated toward the center of the road while the vehicle 10 is traveling (3) When the driving speed of the vehicle 10 changes to slow down (4) When the distance between the vehicle 10 traveling in front or behind the vehicle changes to 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.

In step ST104, the third estimation unit 317 may estimate a traffic flow disturbance based on 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 traffic flow disturbance when the driving state of the vehicle 10 acquired by the second acquisition unit 313 includes a driving state that is equal to or exceeds a threshold amount of change or operation.

Here, the third acquisition unit 314 may acquire facility information.
In this case, the third estimation unit 317 may estimate that there is a disruption in traffic flow if it is estimated that there is a facility in operation within a specified distance from the traveling position of the vehicle 10 based on the location information acquired in step ST101 and the facility information acquired by the third acquisition unit 314.

In step ST105, if the presence of an obstacle is estimated in step ST103, or if a traffic flow disruption is estimated in step ST104, the registration unit 318 registers the estimated position in the map information.

In step ST106, the route guidance unit 319 uses the road map in which at least one of the estimation results (e.g., the position) in steps ST103 and ST104 is registered in step ST105 to provide route guidance from the departure point to the destination. Note that, depending on the embodiment, the processing in step ST106 may not be performed.

Each of the above-mentioned units of the information processing device 30 may be realized as a function of a processing unit of a computer, etc. 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 device 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), respectively, by the processing unit of a computer, etc.
The information processing program can cause a computer to realize each of the above-mentioned functions. The information processing program may be recorded in a non-transitory computer-readable recording medium, such as a memory, a solid-state drive, a hard disk drive, or an optical disk.
As described above, each part of the information processing device 30 may be realized by a computer arithmetic processing device or the like. The arithmetic processing device or the like is configured by, for example, an integrated circuit or the like. Therefore, each part of the information processing device 30 may be realized as a circuit constituting the arithmetic processing device 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 device 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 the arithmetic processing device or the like of a computer.
The communication unit 331, the storage unit 332, and the display unit 333 (output unit) of the information processing device 30 may be realized as a communication function, a storage function, and a display function (output function) including the functions of an arithmetic processing device, etc. The communication unit 331, the storage unit 332, and the display unit 333 (output unit) of the information processing device 30 may be realized as a communication circuit, a storage circuit, and a display circuit (output circuit) by being configured, for example, by an integrated circuit, etc. The communication unit 331, the storage unit 332, and the display unit 333 (output unit) of the information processing device 30 may be realized as a communication device, a storage device, and a display device (output device) by being configured, for example, by being configured by a plurality of devices.

The information processing device 30 can be configured by combining one or any two or more of the above-mentioned units.
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."

[Aspects and Effects of the Present Embodiment]
Next, one aspect of this embodiment and the effects of each aspect will be described. Note that this embodiment is not limited to each aspect described below, and may be realized by appropriately combining each of the above-mentioned parts. Also, the effects described below are examples, and the effects of each aspect are not limited to those described below. Also, each aspect may, for example, achieve at least one of the effects described below.

(Aspect 1)
An information processing device of one embodiment includes a first acquisition unit that acquires vehicle information including image information regarding an image generated by an on-board camera, a first estimation unit that estimates lanes 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 device can estimate the presence of an obstacle by using image information generated by an on-board camera. In recent years, on-board cameras have been used for driving recorders and Vlog (Video Blog) shooting, and are being installed in more and more vehicles. Therefore, since the information processing device acquires image information by using, for example, an on-board camera, which is a more general-purpose device, it is 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.
The information processing device can provide driving assistance for both manned and unmanned vehicles by estimating the presence of an obstacle.

(Aspect 2)
In the information processing device of one aspect, the second estimation unit may estimate whether an obstacle is present based on the image information acquired by the first acquisition unit.
This allows the information processing device to estimate the presence of an obstacle by utilizing image information generated by the vehicle-mounted camera.

(Aspect 3)
In one embodiment of the information processing device, the first acquisition unit acquires vehicle information including feature recognition information regarding features recognized based on measurement results of an on-board sensor, and the second estimation unit may estimate whether an obstacle exists based on the feature recognition information acquired by the first acquisition unit.
In this way, the information processing device can estimate the presence of an obstacle more accurately by further utilizing the feature recognition results by the vehicle-mounted sensor. Also, when the information processing device estimates the presence of an obstacle by further utilizing the feature recognition information, it can reduce the dependency on image analysis processing using image information, thereby reducing the processing load.

(Aspect 4)
In one embodiment of the information processing device, the first estimation unit is capable of estimating at least one of a lane boundary line and a road center line as a lane, and the second estimation unit may estimate whether an obstacle exists within a predetermined distance in the road width direction from the lane boundary line or the road center line.
This enables the information processing device to estimate the presence of an obstacle based on the lane boundary lines and the center line of the road.

(Aspect 5)
In one embodiment of the information processing device, the first estimation unit estimates at least one of a lane boundary line and a center line of the road, as well as an outer side line of the road, as a lane, and the second estimation unit may estimate that an obstacle is present when the outer side line of the road cannot be recognized.
When the outer lane line cannot be recognized, it can be presumed that a vehicle (including a motorcycle) is parked on the outer lane line, or that there is a feature such as a plant, etc. Therefore, when the outer lane line cannot be recognized, the information processing device presumes that an obstacle exists, and can perform a more accurate estimation.

(Aspect 6)
In one embodiment of the information processing device, a first acquisition unit acquires vehicle information including vehicle driving information regarding the driving of the vehicle, and a second acquisition unit acquires, based on the vehicle driving information acquired by the first acquisition unit, at least one of the following driving conditions of the vehicle: a change in the steering angle toward the center of the road when the vehicle is driving, a change in the direction of the turn signal toward the center of the road when the vehicle is driving, a change in the vehicle's driving speed slowing down, and a change in the distance between the vehicle and a vehicle driving in front or behind narrowing.The second estimation unit may estimate the presence of an obstacle based on the driving condition of the vehicle acquired by the second acquisition unit.
When an obstacle is present, the driver of the vehicle is expected to perform an operation to avoid the obstacle. Therefore, when the information processing device further acquires the above-mentioned driving state (predetermined driving information), the information processing device estimates that an obstacle is present, and can perform more accurate estimation. In addition, when the information processing device further uses the vehicle driving information to estimate the presence of an obstacle, the information processing device can reduce the dependency on image analysis processing using only image information, and can reduce the processing load.

(Aspect 7)
The information processing device of one aspect may further include a third estimation unit that estimates a traffic flow disturbance based on the vehicle traveling state acquired by the second acquisition unit.
When an obstacle is present on the road, it is considered that the traffic flow of vehicles will be disrupted in order to avoid the obstacle. At that time, it is considered that the vehicles will be in the above-mentioned predetermined running state. Therefore, the information processing device can estimate the traffic flow disruption based on the running state of the vehicles.
The information processing device can provide driving assistance to both manned and unmanned vehicles by estimating traffic flow disruptions.

(Aspect 8)
In one embodiment of the information processing device, the third estimation unit may estimate that there is a disruption in traffic flow when the vehicle driving condition acquired by the second acquisition unit includes a driving condition that is greater than or equal to a threshold change amount or operation.
It is considered that the driving operation to avoid an obstacle requires a larger amount of driving operation and a larger amount of change in the driving state than when the vehicle is driving along the road (following the road). Therefore, the information processing device can estimate that there is a traffic disturbance when the driving state has a threshold amount of change or operation or more for the above-mentioned predetermined driving state.

(Aspect 9)
An information processing device of one embodiment includes a third acquisition unit that acquires facility information regarding the location and business hours of the facility, the first acquisition unit acquires vehicle information including location information regarding the vehicle's driving location, and the third estimation unit may estimate that there is a disruption in traffic flow when it is estimated that there is a facility that is open within a specified distance from the vehicle's driving location based on the location information acquired by the first acquisition unit and the facility information acquired by the third acquisition unit.
For example, when a facility is open, it is expected that vehicles will be parked in a relatively close area from the facility. In such a case, traffic flow may be disrupted in order to avoid the parked vehicles. Therefore, the information processing device can estimate whether there is a disruption in traffic flow depending on the business status of the facility.

(Aspect 10)
An information processing device of one embodiment may include a memory unit that stores map information regarding a road map, and a registration unit that registers a position based on the estimation in the map information when the second estimation unit estimates the presence of an obstacle and/or when the third estimation unit estimates that there is a disruption in traffic flow.
The information processing device can register locations where it is difficult for a vehicle to travel in a road map (map information). The information processing device can improve the convenience of the user by providing the road map (map information) to the user (e.g., a driver, etc.).

(Aspect 11)
An information processing device according to one embodiment may include a route guidance unit that provides route guidance from a departure point to a destination using a road map in which at least one of locations where the presence of an obstacle is estimated and locations where traffic flow is disrupted is registered.
The information processing device can provide route guidance using roads that are easier for a vehicle to travel on. This can reduce the burden on the driver in relation to driving, and can prevent accidents and the like from occurring.

(Aspect 12)
An information processing device according to one embodiment may include a route guidance unit that provides guidance when traveling on a road using a road map in which at least one of locations where the presence of an obstacle is estimated and locations where traffic flow is disrupted is registered.
As a result, even when the vehicle is driving autonomously and following a road, the information processing device can use a road map of the planned route to estimate whether there is an obstacle on the planned route, i.e., whether there is a location on the route where traffic flow will be disrupted, and if there is an obstacle (if traffic flow is disrupted), can take avoidance measures such as changing lanes in advance.

(Aspect 13)
In one aspect of the information processing method, a computer executes a first acquisition step of acquiring vehicle information including image information relating to an image generated by an on-board camera, a first estimation step of estimating lanes on a road based on the image information acquired by the first acquisition step, and a second estimation step of estimating whether an obstacle exists within a predetermined distance in the road width direction from the lane estimated by the first estimation step.
As a result, the information processing method can achieve the same effects as the information processing device according to the above-described aspect.

(Aspect 14)
One embodiment of an information processing program causes a computer to realize a first acquisition function that acquires vehicle information including image information regarding an image generated by an on-board camera, a first estimation function that estimates lanes on a road based on the image information acquired by the first acquisition function, and a second estimation function that estimates whether an obstacle exists within a predetermined distance in the road width direction from the lane estimated by the first estimation function.
As a result, the information processing program can achieve the same effects as the information processing device according to the above-described aspect.

REFERENCE SIGNS LIST 1 Information processing system 10 Vehicle 20 Server 30 Information processing device 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 (13)

A storage unit for storing map information relating to a road map;
A first acquisition unit that acquires vehicle information including image information related to an image generated by an on-board camera and position information related to a traveling position of the vehicle;
a first estimation unit which estimates lanes on a road based on image information acquired by the first acquisition unit, estimating, as lanes, at least one of a lane boundary line between adjacent lanes of a road with multiple lanes on one side and a center line of a road closest to an oncoming lane of a road with one or more lanes on one side, and an outer side line of a road closest to a shoulder of a road with one or more lanes on one side;
a second estimation unit which estimates whether an obstacle is present within a predetermined distance in a road width direction from the lane estimated by the first estimation unit, and which does not estimate the presence of an obstacle within an intersection based on the position information acquired by the first acquisition unit and the map information stored in the storage unit, and which estimates the presence of an obstacle when an outer side line of a roadway cannot be recognized ;
An information processing device comprising: The information processing device according to claim 1 , wherein the second estimation unit estimates whether an obstacle is present based on the image information acquired by the first acquisition unit. The first acquisition unit acquires vehicle information including feature recognition information regarding features recognized based on a measurement result of an on-board sensor,
The information processing apparatus according to claim 1 , wherein the second estimation unit estimates whether an obstacle is present based on the feature recognition information acquired by the first acquisition unit. The first estimation unit is capable of estimating at least one of a lane boundary line and a road center line as a lane,
4. The information processing device according to claim 1, wherein the second estimation unit estimates whether an obstacle is present within a predetermined distance in a road width direction from a lane boundary line or a center line of the road. The first acquisition unit acquires vehicle information including vehicle driving information related to driving of the vehicle,
a second acquisition unit that acquires at least one of the following vehicle driving conditions based on the vehicle driving information acquired by the first acquisition unit: a change in steering angle toward the center of the road while the vehicle is driving, a change in a vehicle driving speed that slows down, and a change in a vehicle distance to a vehicle driving ahead or behind that narrows;
The second estimation unit estimates the presence of an obstacle based on the traveling state of the vehicle acquired by the second acquisition unit.
The information processing device according to any one of claims 1 to 4 . A third estimation unit is provided for estimating a traffic disturbance based on the vehicle running state acquired by the second acquisition unit.
The information processing device according to claim 5 . The third estimation unit estimates that a traffic flow is disturbed when a vehicle driving state acquired by the second acquisition unit includes a driving state having a change amount or operation equal to or greater than a threshold value.
The information processing device according to claim 6 . a third acquisition unit that acquires facility information related to a location and business hours of the facility;
The third estimation unit estimates that a traffic flow is being disrupted when it is estimated that a facility in operation is present within a predetermined distance from a traveling position of the vehicle based on the location information acquired by the first acquisition unit and the facility information acquired by the third acquisition unit.
8. The information processing device according to claim 6 or 7 . A storage unit for storing map information relating to a road map;
a registration unit that registers a position based on the estimation in map information when at least one of the second estimation unit estimates the presence of an obstacle and the third estimation unit estimates the presence of a disturbance in traffic flow;
The information processing device according to any one of claims 6 to 8 , comprising: A route guidance unit provides route guidance from a departure point to a destination using the map information on a road map in which at least one of positions where an obstacle is estimated to exist and positions where traffic flow is disrupted is registered.
The information processing device according to claim 9 . A route guidance unit is provided that uses the map information on a road map in which at least one of positions where the presence of an obstacle is estimated and positions where traffic flow is disrupted is registered, to provide guidance when traveling on the road.
The information processing device according to claim 9 . A computer including a storage unit for storing map information relating to a road map,
A first acquisition step of acquiring vehicle information including image information relating to an image generated by an on-board camera and position information relating to a traveling position of the vehicle;
a first estimation step for estimating lanes on a road based on image information acquired by the first acquisition step, estimating, as lanes, at least one of a lane boundary line between adjacent lanes of a road with multiple lanes on one side and a center line of a road closest to the oncoming lane of a road with one or more lanes on one side, and an outer side line of a road closest to the shoulder of a road with one or more lanes on one side ;
a second estimation step for estimating whether an obstacle exists within a predetermined distance in a road width direction from the lane estimated by the first estimation step, and for not estimating the presence of an obstacle within an intersection based on the position information acquired by the first acquisition step and the map information stored in the storage unit, and for estimating the presence of an obstacle when an outer lane of a roadway cannot be recognized ;
An information processing method for performing the above. A computer having a storage unit for storing map information relating to a road map,
A first acquisition function that acquires vehicle information including image information related to an image generated by an on-board camera and position information related to a traveling position of the vehicle;
a first estimation function for estimating lanes on a road based on image information acquired by the first acquisition function, estimating, as lanes, at least one of a lane boundary line between adjacent lanes on a road with multiple lanes on one side and a center line on the side closest to the oncoming lane on a road with one or more lanes on one side, and an outer side line on the side closest to the shoulder on a road with one or more lanes on one side ;
a second estimation function that estimates whether an obstacle exists within a predetermined distance in a road width direction from the lane estimated by the first estimation function, and does not estimate the presence of an obstacle within an intersection based on the position information acquired by the first acquisition function and the map information stored in the storage unit, and estimates the presence of an obstacle when the outer side line of the roadway cannot be recognized ;
An information processing program that realizes this.

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