Information providing device, information providing method, and program

Abstract

To provide an information providing device capable of appropriately obtaining a determination result concerning road similarity, and to provide an information providing method and a program.SOLUTION: An information providing device comprises an acquisition part for acquiring feature data comprising a plurality of kinds of information about a first road partition including a first road section and a peripheral area of the first road section, and feature data comprising a plurality of kinds of information about a second road partition including a second road section different from the first road section and a peripheral area of the second road section, a determination part for determining whether or not the first road partition and the second road partition are similar to each other by comparing the feature data about the first road partition with the feature data about the second road partition, and a preparation part for preparing a list relating to the second road partition similar to the first road partition.SELECTED DRAWING: Figure 12

Description

[Technical field]

[0001] The present disclosure relates to an information providing device, an information providing method, and a program. [Background technology]

[0002] In providing information for driving to a vehicle traveling on a road, it is important to appropriately acquire road information and select and provide appropriate information. Patent Document 1 describes an information providing device for alerting a driver by considering a behavior to be careful of detected in the past, the situation when the behavior occurred, and the situation of the road on which the vehicle is scheduled to travel. The information providing device described in Patent Document 1 includes an acquisition unit that acquires first information indicating at least one of the characteristics and weather of the road on which the behavior to be careful of detected in the past occurred, and acquires second information indicating at least one of the characteristics and weather of the planned road on which the vehicle is scheduled to travel. In addition, the information providing device includes a determination unit that determines whether the situation indicated by the first information and the situation indicated by the second information are the same or similar, and a transmission unit that transmits attention information that alerts the driver of the vehicle to the behavior to be careful of when traveling on the planned road when it is determined that the situation indicated by the first information and the situation indicated by the second information are the same or similar.

[0003] In the information providing device described in Patent Document 1, for example, whether the conditions of the comparison targets are similar or not is determined based on the characteristics of the road. In Patent Document 1, the characteristics of the road are acquired, for example, from a server that distributes the conditions at any position on the road. Furthermore, Patent Document 1 appears to give the following items as examples of road characteristics. The items given are that the road is a bridge, that the road is a curve in the mountains, that the road includes the exit of a tunnel, and that the road is a straight road with good visibility. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2019-159411 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the information providing device described in Patent Document 1 does not include in its judgment factors matters related to the target road and its surroundings, such as matters related to other vehicles entering and exiting the target road, and there is a problem in that depending on the use of the judgment result as to whether or not the roads are similar, the judgment may be inappropriate.

[0006] The present disclosure has been made to solve the above-mentioned problem, and aims to provide an information providing device, an information providing method, and a program that can appropriately obtain a determination result about road similarity. [Means for solving the problem]

[0007] In order to solve the above problem, the information providing device of the present disclosure includes an acquisition unit that acquires feature data including multiple types of information about a first road section that includes a first road section and a surrounding area of ​​the first road section, and feature data including the multiple types of information about a second road section that includes a second road section different from the first road section and a surrounding area of ​​the second road section, a determination unit that compares the feature data about the first road section with the feature data about the second road section to determine whether the first road section and the second road section are similar, and a creation unit that creates a list of the second road sections that are similar to the first road section based on the result of the determination.

[0008] The information providing method of the present disclosure includes the steps of acquiring feature data including multiple types of information about a first road segment including a first road section and a surrounding area of ​​the first road section, and feature data including the multiple types of information about a second road segment including a second road section different from the first road section and the surrounding area of ​​the second road section, comparing the feature data about the first road segment with the feature data about the second road segment to determine whether the first road segment and the second road segment are similar, and creating a list of the second road segments that are similar to the first road segment based on the result of the determination.

[0009] The program disclosed herein causes a computer to execute the steps of acquiring feature data including multiple types of information about a first road segment including a first road section and the surrounding area of ​​the first road section, and feature data including the multiple types of information about a second road segment including a second road section different from the first road section and the surrounding area of ​​the second road section, comparing the feature data about the first road segment with the feature data about the second road segment to determine whether the first road segment and the second road segment are similar, and creating a list of the second road segments that are similar to the first road segment based on the result of the determination. Effect of the Invention

[0010] According to the information providing device, the information providing method, and the program of the present disclosure, it is possible to appropriately obtain a determination result regarding the similarity of roads. [Brief description of the drawings]

[0011] [Figure 1] 1 is a system diagram illustrating a configuration example of a driving condition monitoring device according to an embodiment of the present disclosure. [Diagram 2] 1 is a schematic diagram showing an example of the configuration of driving information used in a driving condition monitoring device according to an embodiment of the present disclosure. [Diagram 3]13 is a schematic diagram showing another configuration example of the travel information used in the travel condition monitoring device according to the embodiment of the present disclosure. FIG. [Figure 4] 1 is a schematic diagram showing an example of the configuration of travel section information used in a travel condition monitoring device according to an embodiment of the present disclosure. [Diagram 5] 13 is a flowchart illustrating an example of the operation of a unique determination unit according to an embodiment of the present disclosure. [Figure 6] 11A and 11B are schematic diagrams for explaining an example of the operation of a unique determination unit according to an embodiment of the present disclosure. [Figure 7] 11A and 11B are schematic diagrams for explaining an example of the operation of a unique determination unit according to an embodiment of the present disclosure. [Figure 8] 11A and 11B are schematic diagrams for explaining an example of the operation of a unique determination unit according to an embodiment of the present disclosure. [Figure 9] 13 is a flowchart illustrating an example of the operation of a unique determination unit according to an embodiment of the present disclosure. [Figure 10] 11A and 11B are schematic diagrams for explaining an example of the operation of a unique determination unit according to an embodiment of the present disclosure. [Figure 11] 11A and 11B are schematic diagrams for explaining an example of the operation of a unique determination unit according to an embodiment of the present disclosure. [Figure 12] 1 is a block diagram showing a configuration example of an information providing device according to an embodiment of the present disclosure. [Figure 13] FIG. 2 is a schematic diagram illustrating a configuration example of geographic information according to an embodiment of the present disclosure. [Figure 14] 1 is a schematic diagram illustrating a configuration example of road section list information according to an embodiment of the present disclosure. FIG. [Figure 15] 3 is a schematic diagram showing a configuration example of road division characteristic information according to an embodiment of the present disclosure; FIG. [Figure 16] FIG. 2 is a schematic diagram illustrating a configuration example of road information according to an embodiment of the present disclosure. [Figure 17] 1 is a schematic diagram showing an example of the configuration of road surrounding information according to an embodiment of the present disclosure. FIG. [Figure 18] 1 is a schematic diagram showing an example of the configuration of a road segment list according to an embodiment of the present disclosure. FIG. [Figure 19] FIG. 2 is a block diagram showing a configuration example of a determination unit according to an embodiment of the present disclosure. [Figure 20] 10 is a flowchart illustrating an example of an operation of a determination unit according to an embodiment of the present disclosure. [Figure 21] FIG. 2 is a schematic diagram for explaining a road section according to an embodiment of the present disclosure. [Figure 22] FIG. 1 is a schematic block diagram illustrating a configuration of a computer according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] (Example of configuration of driving condition monitoring device) Hereinafter, a configuration example of a driving condition monitoring device according to an embodiment of the present disclosure will be described with reference to Figs. 1 to 4. Fig. 1 is a system diagram showing a configuration example of a driving condition monitoring device according to an embodiment of the present disclosure. Fig. 2 is a schematic diagram showing a configuration example of driving information used in the driving condition monitoring device according to an embodiment of the present disclosure. Fig. 3 is a schematic diagram showing another configuration example of driving information used in the driving condition monitoring device according to an embodiment of the present disclosure. Fig. 4 is a schematic diagram showing a configuration example of driving section information used in the driving condition monitoring device according to an embodiment of the present disclosure. Note that the same or corresponding configurations in each drawing are designated by the same reference numerals and description thereof will be omitted as appropriate.

[0013] 1, a driving condition monitoring device (or driving condition monitoring system) 100 according to an embodiment of the present disclosure includes a central data device 1, a driving information processing device 2, a road toll determination device 3, a traffic statistics information notifying device 4, a traffic violation determination device 5, a road toll management device 6, a traffic statistics information management device 7, a traffic violation management device 8, an information providing device 500, and a plurality of vehicle-mounted devices 10. Each of the devices 1 to 8 and 500 can be configured using, for example, a server, a personal computer, and their peripheral devices.

[0014] The in-vehicle device 10 includes a GNSS (Global Navigation Satellite System) receiving device 11, an information processing device 12, and a mobile communication device 13. The in-vehicle device 10 is mounted on a plurality of automobiles (mobiles) C1, C2, etc. traveling on a road 301. The in-vehicle device 10 is not limited to being mounted on automobiles, but may be mounted on any moving object such as a bicycle, a two-wheeled vehicle, or a drone. The automobile may be an autonomous vehicle. Furthermore, the comparison of travel information described later may be made between different moving objects, such as an automobile and a two-wheeled vehicle, or a drone and a bicycle.

[0015] The GNSS receiver 11 repeatedly calculates position information (the latitude, longitude, and altitude of the antenna position) at a predetermined period based on GNSS signals received from multiple GNSS satellites 401 using an antenna for receiving GNSS signals, and outputs the calculated position information to the information processing device 12. The GNSS receiver 11 may also have a function of correcting an error in the position information by receiving a GNSS correction signal transmitted by an antenna 402 installed, for example, at the side of a road 301.

[0016] The information processing device 12 transmits a time series (time series data) of the position information acquired from the GNSS receiver 11, for example, repeatedly at regular intervals, to the central data device 1 using the mobile communication device 13. For example, the information processing device 12 may transmit unique information (for example, the type of vehicle, the vehicle number, etc.) of the automobile C1 or the like mounting the in-vehicle device 10 together with the position information to the central data device 1. Note that the position information calculated by the GNSS receiver 11 is hereinafter also referred to as the position information of the in-vehicle device 10 or the position information of the automobile (C1, etc.).

[0017] The mobile communication device 13 is connected to a mobile base station 403 or the like to communicate with the central data unit 1 via a communication network such as a mobile communication network.

[0018] The central data unit 1 collects time series of all position information for a certain period of time transmitted by a plurality of vehicle-mounted units 10 and stores it in the storage device 1-1. The central data unit 1 stores the time series of each position information, for example, using an individual identification number or the like set in the mobile communication device 13 as an identification code (vehicle identification code) of each vehicle-mounted unit 10. The central data unit 1 also stores the driving information generated by the driving information processing device 2 in the storage device 1-1. The central data unit 1 also provides the driving information stored in the storage device 1-1 to the road toll determination device 3, the traffic statistics information notification device 4, and the traffic violation determination device 5. At that time, the central data unit 1 can provide the driving information, for example, by recording the vehicle and speed based on the driving information on a map (adding it to the map data).

[0019] (Example of configuration of the driving information generating unit 21) The driving information processing device 2 includes a driving information generating unit 21 as a functional configuration formed by a combination of hardware such as a computer and software such as a program executed by the computer. The driving information generating unit 21 generates "driving information" that is information that represents the "driving situation" of the automobile C1 for each predetermined "driving section 310" based on a time series of position information of the automobile C1, which is an example of an evaluation target vehicle. The driving information generating unit 21 then transmits the generated driving information to the central data device 1 and stores it in the central data device 1.

[0020] The "traveling section 310" is a portion of the road 301 obtained by dividing the road 301 into a plurality of sections at predetermined distances, or by dividing the road 301 into a plurality of sections corresponding to the shape of the road 301, such as straight lines, curves, slopes, etc. Adjacent traveling sections 310 may or may not have overlapping portions.

[0021] The "driving conditions" are information that indicates the driving mode of the vehicle, the surrounding environment of the vehicle, and the like, and include, for example, information on the driving time, driving time zone, driving speed, driving acceleration (forward / backward, left / right), weather during driving, driving trajectory (for example, the time series of position information itself), passing time of the target section, driving direction, and the like. The driving information generating unit 21 acquires a time series of position information of each vehicle from the central data device 1, identifies one or more driving sections based on the acquired time series of position information, and generates driving information for each driving section by associating the driving section with the driving conditions. In this case, the driving information generating unit 21 refers to, for example, map information or driving section information described later, identifies the driving section based on the position information, and generates driving information for each identified driving section. The driving information generating unit 21 acquires the weather during driving, for example, from a server that provides external weather information (not shown).

[0022] Fig. 2 shows an example of the configuration of driving information (driving information 201) generated by the driving information generating unit 21. The driving information 201 shown in Fig. 2 includes a driving section identification code which is identification information of a driving section 310, a vehicle identification code which is identification information of an automobile or an in-vehicle device, and a position information time series. The position information time series is, for example, a time series of position information (a set of data representing date and time, latitude, and longitude) corresponding to the driving section.

[0023] Fig. 3 shows another example of the configuration of the driving information (driving information 202) generated by the driving information generating unit 21. The driving information 202 shown in Fig. 3 includes a driving section identification code which is identification information of a driving section 310, a driving direction (traffic direction when the driving section is two-way traffic), a vehicle identification code which is identification information of an automobile or an in-vehicle device, a date and time (year, month, day, hour, minute, second), weather indicating meteorological conditions such as cloud cover, rainfall, snowfall, temperature, and the presence or absence of fog around the driving section, speed (driving speed (average, minimum, maximum)), acceleration (driving acceleration (front / rear, left / right)), and a position information time series. However, for example, a part of the position information time series, etc. may be omitted.

[0024] Fig. 4 shows an example of the configuration of driving section information (driving section information 203) that the driving information generating unit 21 refers to when identifying a driving section. The driving information processing device 2 stores each driving section information for each driving section. The driving section information 203 shown in Fig. 4 includes a driving section identification code that is identification information for the driving section 310, a driving direction (traffic direction when the driving section is two-way traffic), coordinate information (a group of coordinates of latitude and longitude of the boundary) that indicates the range of the driving section, a curvature (average, maximum) of the driving section, and a speed limit.

[0025] The road toll determination device 3 shown in FIG. 1 acquires the driving information stored in the central data device 1, and based on the driving information, determines whether or not driving that places a load on the road 301 or driving that adversely affects vehicles traveling in the vicinity (driving including sudden acceleration, sudden deceleration, speeding, etc.) has occurred, and notifies the road toll management device 6 of the determination result. Alternatively, the road toll determination device 3 may determine whether or not driving that avoids a specific lane has occurred, for example, when an obstacle such as a fallen object exists on the road 301, based on the driving information, and notifies the road toll management device 6 of the determination result. The road toll management device 6 is, for example, a device operated by a business that manages the road 301, and based on the instruction of the operator, refers to the determination result of the road toll determination device 3, and performs toll management such as charging a certain surcharge to a specific vehicle, or makes arrangements to patrol the location of the occurrence when it is determined that a fallen object exists.

[0026] 1 acquires the travel information stored in the central data device 1, and notifies the traffic statistics information management device 7 of information representing a map created based on the travel information to make it easy to grasp the occurrence of congestion by, for example, displaying the number of vehicles and average speed on a map. The traffic statistics information management device 7 is, for example, a device operated by a local government that manages the road 301 or a business operator commissioned by the local government or the like, and displays, edits, and prints, for example, the information notified from the traffic statistics information notification device 4 based on instructions from an operator.

[0027] (Example of configuration and operation of the singular determination unit 51) The traffic violation determination device 5 includes a peculiar determination unit 51 as a functional configuration formed by a combination of hardware such as a computer and software such as a program executed by the computer. The peculiar determination unit 51 determines whether the driving of the automobile C1 is peculiar, for example, corresponding to dangerous driving, by comparing driving information of the automobile C1, which is an example of a vehicle to be evaluated, with driving information of the automobile C2, which is an example of other vehicles. The traffic violation determination device 5 provides the result of the determination to the traffic violation management device 7.

[0028] The peculiar determination unit 51 can determine whether the driving of the evaluation target vehicle is peculiar or not based on, for example, whether there is a difference of a certain amount or more between the values ​​by comparing a value based on the left-right movement based on the driving information of the evaluation target vehicle with a value based on the statistics of the left-right movement based on the driving information of the other vehicle, and when it is determined that the driving of the evaluation target vehicle is peculiar or not based on, for example, whether there is a difference of a certain amount or more between the values, and when it is determined that the driving of the evaluation target vehicle is peculiar or not based on, for example, whether there is a difference of a certain amount or more between the values ​​by comparing a passing speed of the evaluation target vehicle based on the driving information of the evaluation target vehicle with a value based on the statistics of the passing speed of the evaluation target vehicle based on the driving information of the other vehicle, and when it is determined that the driving of the evaluation target vehicle is peculiar or not based on, for example, whether there is a difference of a certain amount or more between the values ​​... However, when the traveling speed of the vehicle to be evaluated and the vehicle ahead is lower than the traveling speed of the surrounding vehicles by a predetermined value or more, or is lower than a predetermined ratio, the peculiar determination unit 51 can cancel the determination that the driving is dangerous without maintaining a safe distance between the vehicles, and determine that the driving of the vehicle ahead is dangerous due to low speed driving. In addition to a comparison using a predetermined value, the determination of whether or not the driving is peculiar may be made by artificial intelligence. Specifically, data corresponding to dangerous driving such as meandering, speeding, and not maintaining a safe distance between the vehicles may be learned in advance, and the traveling information of the vehicle to be evaluated may be compared with the learned data to determine whether or not the driving is dangerous such as meandering, speeding, and not maintaining a safe distance between the vehicles.

[0029] Hereinafter, a process in which the peculiar determination unit 51 determines peculiar driving corresponding to dangerous driving will be described. FIG. 5 is a flowchart showing an example of the operation of the peculiar determination unit 51 according to the embodiment of the present disclosure. FIG. 5 shows an example of a basic operation of the peculiar determination unit 51. In the example of operation shown in FIG. 5, first, the peculiar determination unit 51 acquires driving information of the evaluation target vehicle from the central data device 1 (step S101). Next, the peculiar determination unit 51 acquires driving information of other vehicles in the same driving section, the same time period, and the same weather from the central data device 1 (step S102). Next, the peculiar determination unit 51 determines whether the driving of the evaluation target vehicle is peculiar or not by comparing the driving information of the evaluation target vehicle with the driving information of the other vehicles (step S103). Then, if the driving of the evaluation target vehicle is peculiar (if "Yes" in step S103), the peculiar determination unit 51 determines that the evaluation target vehicle is driving dangerously (step S104).

[0030] In addition, when the peculiar judgment unit 51 judges whether the vehicle alone (the vehicle to be evaluated alone) has engaged in dangerous driving, it compares the driving information of the vehicle alone in any driving section with map data to which speed limits and lane information have been added, or with driving information of other vehicles (including past driving information) at the same time and under the same weather conditions, and if the driving of the vehicle alone is a traffic violation or peculiar compared to other vehicles, it can judge that the vehicle has engaged in dangerous driving. By changing the items that are judged as dangerous driving depending on the road shape, dangerous driving can be detected flexibly. For example, meandering driving can be used as a judgment item for dangerous driving on straight roads, and driving speed can be used on curved sections.

[0031] In the case of meandering driving, the determination of whether or not it is peculiar can be performed by, for example, clustering the statistics of past data of driving information (left and right movement relative to the traveling direction, variance, second and fourth moments, distribution shape, image recognition of data graphs, etc.) by machine learning, and classifying it into normal driving and dangerous driving using a learned model. The presence or absence of dangerous driving can be determined from the statistics of the vehicle to be determined using the learned parameters. FIG. 6 is a schematic diagram for explaining an example of the operation of the peculiar determination unit according to the embodiment of the present disclosure. FIG. 6 shows a comparison between the driving (left side) of an automobile C1, which is an evaluation target vehicle, and the driving (right side) of multiple other vehicles C2 in the same driving section 310a of the same road 301a. By classifying the driving information of the automobile C1 using a learned model learned based on the driving information of multiple other vehicles C2, the peculiar determination unit 51 can determine whether or not the driving of the evaluation target vehicle is peculiar by comparing the driving information of the evaluation target vehicle with the driving information of the other vehicles.

[0032] In addition, if the speed is excessive, it can be determined that the driving is dangerous if the driving speed is faster than μ+2σ (average+twice the standard deviation) for a certain period of time based on the statistics of past data of the driving information. Figures 7 and 8 are schematic diagrams for explaining an example of the operation of the peculiar determination unit 51 according to the embodiment of the present disclosure. Figure 7 shows an example of the driving of an automobile C1, which is an evaluation target vehicle, in a driving section 310b of a road 301b, and Figure 8 shows an example of the driving of a plurality of other vehicles C2 in the same driving section 310b of the same road 301b.

[0033] As described above, the peculiar determination unit 51 calculates statistics from the time series of the obtained position information, and can determine whether a vehicle is peculiar based on the results of class classification such as clustering, or on outliers from the average value of the statistics (values ​​that deviate from the standard deviation by a predetermined value or more, outliers when plotting a box-and-whisker plot, etc.). Examples of behaviors that are determined to be peculiar include behaviors that periodically deviate from the traveling direction to the left and right like a sine wave, behaviors in which the distance between the vehicle and the vehicle ahead is short or the vehicle approaches and moves away from the vehicle ahead, and behaviors that accelerate and decelerate significantly.

[0034] Furthermore, the peculiar determination unit 51 extracts past data to be compared (at the same weather time) based on weather and time information. However, if the amount of extracted past data is insufficient as the amount of data for comparison, the peculiar determination unit 51 calculates a statistical difference (amount of difference) between the same weather time and other areas with similar road structures. The peculiar determination unit 51 then adjusts the value of the statistical amount using the obtained amount of difference and past data at times other than the same weather, thereby making it possible to deal with a data shortage. In this case, the peculiar determination unit 51 can, for example, identify a road section, inquire of the information providing device 500 about information on other road sections similar to the identified road section, and obtain from the information providing device 500 a notification including, for example, information showing a list of other similar road sections. Here, the road section corresponds to a comparison unit when the information providing device 500 determines the similarity of roads. Note that the information providing device 500 determines the similarity of a road section with other road sections by using a road section including the road section as a comparison unit, as described later. The road section is a part of a road, and can be set according to the distance, shape, etc., in the same way as the travel section 310. The road section may be the same as the travel section 310, or may be partially or entirely different. That is, the travel section 310 and the road section may be the same, one travel section 310 may correspond to multiple road sections, or one road section may correspond to multiple travel sections 310. The information for identifying the road section may be a travel section identification code, location information of the travel section or road section, a combination of a road name and a section name, road partition identification information described later, or the like. For example, when data on late night and rain in a certain analysis area is insufficient, the peculiar determination unit 51 compares the statistics under the same weather time conditions in other areas (areas with similar road structures) with those under other conditions. The error ΔS of the statistics is recorded, and when the analysis area has the same weather time conditions, the statistics under the same weather time conditions are defined as the statistics under the same weather time conditions, which are calculated by adding ΔS to the statistics under the other cases. In other words, the peculiar determination unit 51 can determine whether the driving of the vehicle to be evaluated is peculiar or not, for example, by comparing a predetermined value based on the driving information of the vehicle to be evaluated with a value that is a statistically corrected statistical quantity based on the driving information of other vehicles linked to a similar driving section.

[0035] Next, regarding the judgment of dangerous driving involving the own vehicle and another vehicle (e.g., failure to maintain a distance between vehicles), the peculiar judgment unit 51 compares the driving information of the own vehicle and other related vehicles in any section or driving time with the driving information (including past) of other vehicles in the same time period and weather conditions, and if the distance between the other vehicle is significantly short, it can judge the own vehicle as dangerous driving due to failure to maintain a distance between vehicles. On the other hand, if the peculiar judgment unit 51 judges that the distance between the vehicle and the vehicle in front is short and that the distance between the vehicle is not maintained, but the driving speed of the own vehicle and the vehicle in front is significantly slower than the driving speed of the surrounding vehicles, it can judge that the distance between the own vehicle and the vehicle in front has become short due to the low speed driving of the vehicle in front, and can cancel the failure to maintain a distance between the own vehicle and the vehicle in front and judge that the driving is dangerous due to the low speed driving of the vehicle in front.

[0036] FIG. 9 is a flowchart showing an example of the operation of the peculiar determination unit 51 according to the embodiment of the present disclosure. FIG. 9 shows an example of the operation of the peculiar determination unit 51 in which dangerous driving is determined by failure to maintain a vehicle-to-vehicle distance. In the example of the operation shown in FIG. 9, first, the peculiar determination unit 51 acquires driving information of the evaluation target vehicle from the central data device 1, and acquires the driving position and driving speed of the evaluation target vehicle (step S201). Next, the peculiar determination unit 51 acquires driving information of a vehicle ahead of the evaluation target vehicle from the central data device 1, and acquires the driving position and driving speed of the forward vehicle (step S202). Next, the peculiar determination unit 51 judges whether the driving speeds of the evaluation target vehicle and the forward vehicle are approximately equal (step S203). If the driving speeds are not approximately equal (if "No" in step S203), the peculiar determination unit 51 returns to step S201 and acquires new driving information.

[0037] On the other hand, if the traveling speeds are almost the same (if "Yes" in step S203), the peculiar judgment unit 51 calculates the distance between the vehicle to be evaluated and the vehicle ahead (step S204). Next, the peculiar judgment unit 51 judges whether the distance between the vehicles is significantly short or not (step S205). If the distance between the vehicles is not significantly short (if "No" in step S205), the peculiar judgment unit 51 returns to step S201 and acquires new traveling information. On the other hand, if the distance between the vehicles is significantly short (if "Yes" in step S205), the peculiar judgment unit 51 acquires the traveling speeds of the surrounding vehicles at the same time (step S206).

[0038] Next, the peculiar judgment unit 51 judges whether the traveling speed of the evaluation target vehicle is approximately equal to the traveling speed of the surrounding vehicles at the same time (step S207). If the traveling speeds are not approximately equal (if "No" in step S207), the peculiar judgment unit 51 judges that the preceding vehicle is driving dangerously at a low speed (step S208), whereas if the traveling speeds are approximately equal (if "Yes" in step S207), the peculiar judgment unit 51 judges that the evaluation target vehicle is driving dangerously by not maintaining a vehicle distance (step S209).

[0039] 10 and 11 are schematic diagrams for explaining an example of the operation of the peculiar determination unit 51 according to an embodiment of the present disclosure. FIG. 10 and FIG. 11 show an example of non-maintenance of a vehicle distance. FIG. 10 shows a driving example (left side) of an automobile C1, which is an evaluation target vehicle, and a plurality of other vehicles C21, C22, and C23 at a certain time in the same driving section 310c of the same road 301c, and a driving example (right side) after a predetermined time. The vehicle distance between the automobile C1 and the preceding automobile C21 is extremely short (a state below a predetermined threshold value corresponding to the driving speed). The driving speed of the automobile C1 and the plurality of other vehicles C21, C22, and C23 is 80 km / h. In the example shown in FIG. 10, the peculiar determination unit 51 determines that the evaluation target vehicle C1 is driving dangerously by not maintaining a vehicle distance ("Yes" in step S203 → "Yes" in step S205 → "Yes" in step S207 → step S209).

[0040] FIG. 11 shows an example of driving (left side) of an automobile C1, which is an evaluation target vehicle, and multiple other vehicles C21, C22, and C23 at a certain time in the same driving section 310c of the same road 301c, and an example of driving (right side) of the same predetermined time later. The inter-vehicle distance between the automobile C1 and the preceding automobile C21 is extremely short (a state below a predetermined threshold value corresponding to the driving speed). The driving speed of the automobile C1 and the multiple other vehicles C21 is 40 km / h. The driving speed of the automobiles C22 and C23 is 80 km / h. In the example shown in FIG. 11, the peculiar judgment unit 51 judges that the preceding vehicle C21 is driving dangerously at a low speed ("Yes" in step S203->"Yes" in step S205->"No" in step S207->step S208).

[0041] (Information providing device 500) Next, the information providing device 500 shown in FIG. 1 will be described with reference to FIG. 12 to FIG. 21. FIG. 12 is a block diagram showing a configuration example of the information providing device 500 according to an embodiment of the present disclosure. FIG. 13 is a schematic diagram showing a configuration example of geographic information according to an embodiment of the present disclosure. FIG. 14 is a schematic diagram showing a configuration example of road section list information according to an embodiment of the present disclosure. FIG. 15 is a schematic diagram showing a configuration example of road section characteristic information according to an embodiment of the present disclosure. FIG. 16 is a schematic diagram showing a configuration example of road information according to an embodiment of the present disclosure. FIG. 17 is a schematic diagram showing a configuration example of road surrounding information according to an embodiment of the present disclosure. FIG. 18 is a schematic diagram showing a configuration example of a road section list according to an embodiment of the present disclosure. FIG. 19 is a block diagram showing a configuration example of a determination unit according to an embodiment of the present disclosure. FIG. 20 is a flowchart showing an operation example of the determination unit according to an embodiment of the present disclosure. FIG. 21 is a schematic diagram for explaining a road section according to an embodiment of the present disclosure. The information providing device 500 has a functional configuration constituted by a combination of hardware such as a computer and software such as a program executed by the computer, and includes an acquisition unit 501, a determination unit 502, a creation unit 503, an adjustment unit 504, a notification unit 505, and a storage unit 506. The storage unit 506 stores geographic information 507, road segment list information 508, road segment characteristic information 509, and a road segment list 510.

[0042] First, the geographic information 507, road section list information 508, road section characteristic information 509, and road section list 510 stored in the storage unit 506 will be described with reference to Figs. 13 to 18 and 21. As shown in Fig. 13, the geographic information 507 includes three-dimensional map information 5071 and geospatial information 5072. The three-dimensional map information 5071 is information that represents a three-dimensional map. The geospatial information 5072 is information about a predetermined phenomenon associated with the position information of a specific point or area in the space indicated by the three-dimensional map information 5071. The geospatial information 5072 includes, for example, road information (shape, structure (number of lanes, etc.), road importance classification, arbitrary road classification, number of connecting roads, etc.), road surrounding information (habitable area ratio, habitable area population density, forest rate, wilderness rate, etc.), hourly traffic volume, weather information, etc. Furthermore, the geospatial information 5072 may include, for example, thematic maps such as land use maps, geological maps, and hazard maps, urban planning maps, topographical maps, place name information, statistical information, aerial photographs, satellite images, etc. Note that the weather information includes, for example, information on the weather at each predetermined time in the past, the current weather, and a weather forecast for each predetermined time in the future.

[0043] As shown in FIG. 14, the road segment list information 508 includes a plurality of road segment data 5081. The road segment list information 508 includes, for example, road segment data 5081 related to all road segments to be processed. The road segment data 5081 includes road segment identification information 5082, road segment position information 5083, and road segment identification information 5084. In this embodiment, the information providing device 500 uses a road segment as a comparison unit to determine the similarity of the road segments included in each road segment. Each road segment usually includes a road segment that is a part of the road 301 and a surrounding area of ​​the road segment. In this embodiment, the information providing device 500 determines that the first road segment and the second road segment are similar when a road segment (hereinafter referred to as the first road segment) including a road segment to be compared (hereinafter referred to as the first road segment) is similar to a road segment (hereinafter referred to as the second road segment) including another road segment (hereinafter referred to as the second road segment). FIG. 21 is a plan view that shows a model of a road segment 600 in this embodiment. The road segment 600 includes a road section 601. The road section 601 is a part of the road 301d. The road segment 600 includes the road section 601 and surrounding areas 602 and 603 of the road section 601. One road segment 600 includes one road section 601 and zero, one or multiple surrounding areas. The surrounding areas 602 and 603 are adjacent areas within an arbitrary distance L1 from the road section 601, for example. However, for example, if the road section 601 is in a tunnel, on water, or the like, the road segment 600 may not include the surrounding areas. Note that the distance L1 corresponds to the width of the road in the direction perpendicular to the traveling direction. The road section 601 shown in FIG. 21 also includes a planar crossroad with the road 301e and a planar T-junction with the road 310f. In this case, the roads 310e and 310f are connected to the road section 601, and the number of roads connected to the road section 601 is two. The road segment identification information 5082 is information that uniquely identifies each road segment 600, and is composed, for example, of the road name, road identification information, and road section identification information. The road segment position information 5083 is position information of the boundaries of the road segment 600, and is composed, for example, of the position information of the boundaries of the road section 601, and the position information of each boundary of the surrounding areas 602 and 603. The road section identification information 5084 is information that uniquely identifies the road section 601, and is identification information of the road section 601 that the road segment 600 includes.The road segments 600 can be defined for each direction of travel. For example, if the road 301d shown in Fig. 21 is a two-way road, two road sections 601 are set for each direction of travel in the example shown in Fig. 21. In this case, two road segments 600 can be set for each road section 601 for the two road sections 601 with different directions of travel in the example shown in Fig. 21.

[0044] 15, the road segment characteristic information 509 includes a plurality of road segment characteristic data 5091. Each road segment characteristic data 5091 includes a plurality of types of information about each road segment. The plurality of types of information is, for example, a plurality of types of information that quantifies the characteristics of each road segment. Each road segment characteristic data 5091 includes, for example, road segment identification information 5082, road information 5093, road surroundings information 5094, hourly traffic volume 5095, and weather information 5096.

[0045] The road information 5093 is information on the characteristics of the road section 601 included in the road section 600 having the road section identification information 5082. The road information 5093 includes, for example, a road shape 50931, a number of lanes 50932, a main road section 50933, a number of main road connections 50934, and the like, as shown in FIG. 16. The road shape 50931 can be expressed, for example, by the straightness ratio of the road section 601. The number of lanes 50932 is the number of lanes on one side or both sides of the road section 601. The number of lanes 50932 is an element that represents the structure of the road. The main road section 50933 is an index value according to the importance of the road. The value of the main road section 50933 can be set to a larger value according to the importance, for example, "3" for a first-class national highway stipulated in the old Road Act, "2" for a second-class national highway or a main prefectural road, "1" for other general national highways and main ward city / town / village roads, and "0" for the rest, so that the value is larger as the importance increases. The main road connection number 50934 can be the number of connections of the road section 601 to other roads, for example, "1" or more, in the main road classification 50933. The road information 5093 may also include information such as classification of roads by a combination of road type (whether it is a national expressway or a motorway, or other roads) and region (rural or urban), classification of roads by a combination of road type (general national road, prefectural road, or municipal road), regional topography (flat or mountainous), and planned traffic volume, classification by design vehicle, and index value corresponding to classification by design speed, as specified in the Road Structure Act. The main road connection number 50934 is an example of information related to other roads connecting to the road section 601. However, the information related to other roads connecting to the road section 601 may be, for example, information in which the shape of an intersection with other roads (such as a three-way intersection, a crossroad, or a five-way intersection) is quantified according to the difficulty of passage, etc.

[0046] The road surrounding information 5094 is information on the characteristics of the surrounding areas 602 and 603 of the road section 601 included in the road section 600 having the road section identification information 5082. The road surrounding information 5094 includes, for example, a habitable area area ratio 50941, a habitable area population density 50942, a forest rate 50943, a wilderness rate 50944, and the like, as shown in FIG. 17 . The habitable area area ratio 50941 is the ratio of the habitable area to the area of ​​the surrounding areas 602 and 603. The habitable area population density 50942 is the population density of the surrounding areas 602 and 603. The forest rate 50943 is the ratio of the forest area to the area of ​​the surrounding areas 602 and 603. The wilderness rate 50944 is the ratio of the wilderness area to the area of ​​the surrounding areas 602 and 603. Habitable land area ratio 50941, habitable land population density 50942, forest rate 50943, and wilderness rate 50944 are examples of information corresponding to the ratio of urban areas to the surrounding area. Here, urban areas refer to bustling areas where houses, commercial facilities, public facilities, etc. are densely packed together and where there is almost no farmland or forest land.

[0047] The hourly traffic volume 5095 is, for example, a statistical value of the traffic volume (time series data of the number of passing vehicles) for each hour from 0 to 24. The traffic volume can be classified as data, for example, by weekdays and holidays, or by day of the week.

[0048] The weather information 5096 is statistical data relating to, for example, past weather for the road section 600. The weather information 5096 may be data representing, for example, monthly maximum, average or minimum temperatures, amount of precipitation, amount of snowfall, amount of accumulated snow, and the like.

[0049] The road segment list 510 is a list of similar road segments. For example, as shown in FIG. 18, the road segment list 510 includes a list of road segment identification information of other similar road segments in association with each road segment identification information. In the example shown in FIG. 18, the road segment of the road segment identification information "Road X1 Road Section Y1" and the road segment of the road segment identification information "Road X2 Road Section Y2" are similar to the road segment of the road segment identification information "Road A Road Section 18". The road segment of the road segment identification information "Road X5 Road Section Y5" is similar to the road segment of the road segment identification information "Road A Road Section 6". Note that multiple types of road segment lists 510 may be prepared according to the purpose and the contents of the processing to be used, for example, according to the degree of compatibility described later.

[0050] The information providing device 500 acquires geographic information 507 at a predetermined period from one or more servers (not shown) that provide, for example, three-dimensional map information or geospatial information, and stores the information in the storage unit 506. The information providing device 500 also stores road segment list information 508 in the storage unit 506, for example, in response to an input operation by an operator. The information providing device 500 also generates road segment characteristic information 509 based on the geographic information 507 and the road segment list information 508 in response to an input operation by an operator, and stores the information in the storage unit 506. The information providing device 500 also creates a road segment list 510 by a creation unit 503, and stores the information in the storage unit 506.

[0051] Next, the acquisition unit 501, the determination unit 502, the creation unit 503, the adjustment unit 504, and the notification unit 505 shown in FIG. 12 will be described.

[0052] The acquisition unit 501 acquires from the storage unit 506 road segment characteristic data 5091 (characteristic data) including multiple types of information about a first road segment (road segment 600) including a first road segment (road segment 601) to be compared and a peripheral area (peripheral area 602, etc.) of the first road segment, and road segment characteristic data 5091 (characteristic data) about a second road segment (other road segment 600) including a second road segment (other road segment 601) to be compared different from the first road segment and a peripheral area (other peripheral area 602, etc.) of the second road segment from the storage unit 506, and stores the data in, for example, a main memory of the information providing device 500. The acquisition unit 501 may acquire a part or all of the characteristic data corresponding to the road segment characteristic data 5091 from the central data device 1, one or more servers (not shown), etc.

[0053] The determination unit 502 compares road section feature data 5091 for the first road section with road section feature data 5091 for the second road section to determine whether the first road section and the second road section are similar. An example of the configuration and operation of the determination unit 502 will now be described with reference to Figures 19 and 20. In the configuration shown in Figure 19, the determination unit 502 includes a determiner 5021, a comparator 5022, and a compatibility calculation unit 5023.

[0054] The determiner 5021 is configured using, for example, a trained machine learning model, and inputs the road segment feature data 5091 for the first road segment and the road segment feature data 5091 for the second road segment as explanatory variables, and outputs the similarity as a target variable. In this case, the determiner 5021 is, for example, a trained model with a neural network as an element, and weighting coefficients between neurons in each layer of the neural network are optimized by machine learning so that a desired solution is output for a large amount of input data. The determiner 5021 is, for example, configured by a combination of a program that performs calculations from input to output and weighting coefficients (parameters) used in the calculations. The determiner 5021 is a trained model that is trained by machine learning using as teacher data a plurality of sets of data sets in which a similarity value to be output to a set of the road segment feature data 5091 for the first road segment and the road segment feature data 5091 for the second road segment that are input is labeled. In this case, the similarity value is 0 to 1, with the maximum similarity being 1. The determiner 5021 may input all of the multiple types of information contained in the road division characteristic data 5091, or may input only a part of the information.

[0055] For example, to illustrate a judgment image by the judger 5021, if the road section characteristic data 5091 for the first road section is {road section: road A, road section 1, road shape (straightness ratio): 0.98, number of lanes: 6, main road division: 3, number of main road connections: 3, habitable area ratio: 0.4, hourly traffic volume: [100, 150, ...]} and the road section characteristic data 5091 for the second road section is {road section: road B, road section 3, road shape (straightness ratio): 0.83, number of lanes: 4, main road division: 2, number of main road connections: 5, habitable area ratio: 0.5, hourly traffic volume: [100, 100, ...]}, the similarity between the two is, for example, 0.65. The main road division, number of main road connections, habitable area ratio, and hourly traffic volume are as in the above example.

[0056] As another example of a judgment made by the judger 5021, if the road section feature data 5091 for the first road section is {road section: road A, road section 1, road shape (straightness ratio): 0.98, number of lanes: 6, main road classification: 3, number of main road connections: 3, habitable area ratio: 0.4, hourly traffic volume: [100, 150, ...]} and the road section feature data 5091 for the second road section is {road section: road C, road section 5, road shape (straightness ratio): 0.51, number of lanes: 2, main road classification: 2, number of main road connections: 0, habitable area ratio: 0.01, hourly traffic volume: [10, 20, ...]}, the similarity between the two is, for example, 0.28.

[0057] Comparator 5022 compares the similarity output by determiner 5021 with a similarity determination threshold, and outputs a determination result that the two road sections are similar if the similarity is greater than the similarity determination threshold, and outputs a determination result that the two road sections are dissimilar if the similarity is equal to or less than the similarity determination threshold. For example, if the similarity determination threshold is "0.6", then road section: Road A road section 1 and road section: Road B road section 3, which have a similarity of 0.65 in the above example, are determined to be similar, and road section: Road A road section 1 and road section: Road C road section 5, which have a similarity of 0.28, are determined to be dissimilar.

[0058] The compatibility calculation unit 5023 receives the road section characteristic data 5091 for the first road section and the road section characteristic data 5091 for the second road section, calculates and outputs the compatibility between the first road section and the second road section. The compatibility is an index value that represents the result of comparing the degree of agreement of each type of information by weighting differently for each type of information contained in the road section characteristic data 5091 according to, for example, what kind of processing or purpose the similar roads are used for. For example, if a higher weighting is set for the road shape than for the others, the processing contents of the compatibility calculation unit 5023 can be set so that the smaller the difference in the numerical data of the road shape, the higher the compatibility, and the larger the difference, the lower the compatibility.

[0059] The determination unit 502, for example, periodically repeatedly executes similarity determination for each road segment that is a processing target of the information providing device 500. Fig. 20 shows an example of the operation of the determination unit 502. In the process shown in Fig. 20, the determination unit 502 calculates the similarity between the first road segment and the second road segment (step S301), determines whether or not the similarity determination threshold is exceeded (step S302), and if the similarity determination threshold is exceeded (step S303: Yes), determines that they are similar (step S303).

[0060] As described above, the items that the determination unit 502 focuses on in the similarity determination are, for example, the following: (1) The shape, structure (e.g., number of lanes), and road importance classification (whether treated as a trunk road) of a target road in a certain road section. (2) The number of roads and major roads that connect to a target road in a certain road section. (3) Major roads (e.g., major regional roads and above) that connect to the target road. (4) The habitable land area ratio and habitable land population density in a certain road section. (5) The ratio of forests and wilderness areas adjacent to a target road in a certain road section. (6) The traffic volume at that time of day.

[0061] Based on the result of the determination by the determination unit 502, the creation unit 503 creates and updates the road segment list 510, which is a list related to the second road segment similar to the first road segment, and stores it in the storage unit 506. Note that the creation unit 503 may create multiple types of road segment lists 510 according to the purpose or the content of the process to be used, for example, according to the degree of compatibility. Examples of multiple types of lists include a list used for detecting aggressive driving, a list used for detecting excessive speed, etc.

[0062] The adjustment unit 504 adjusts the number of similar road segments associated with each road segment in the road segment list 510. In the road segment list 510, for example, the length of the list related to a specific road segment determined to be a similar road segment may become long. In such a case, it becomes impossible to determine which road should be adopted. Therefore, when the length of the list of similar road segments exceeds a certain threshold, a more detailed analysis is performed between similar road segments to shorten the length of the list of similar road segments. For example, when the threshold is set to 20, the number of similar road segments associated with each road segment in the road segment list 510 shown in FIG. 18 is limited to 20. Examples of methods for reducing the length of the list include dividing the list by clustering based on statistics and selecting the number of similarities from the highest to the limited number.

[0063] In response to an inquiry including information identifying the first road section 601, the notification unit 505 refers to the road section list 510 and notifies the information corresponding to the similar second road section 601. Alternatively, in response to an inquiry including information identifying the first road section 601 and information identifying the purpose or the content of the processing to be used, the notification unit 505 refers to the road section list 510 suitable for the purpose or the content of the processing to be used and notifies the information corresponding to the similar second road section 601. In the driving condition monitoring device 100 shown in FIG. 1, for example, the peculiar determination unit 51 specifies the coordinates of the target road as information identifying the target road and requests the notification unit 505 to send a list of roads similar to the road corresponding to the coordinates. In response to this, the notification unit 505 specifies the road section 601 corresponding to the specified coordinates and transmits to the peculiar determination unit 51 information indicating a list of road sections 601 included in the road section 600 that is associated in the road section list 510 as being similar to the road section 600 including the road section 601. The information indicating the list of road sections 601 can be a list of road names, road section identification information, information indicating road names and boundary coordinates or center coordinates of road sections, etc. If the road section list 510 does not contain other similar road section lists 510, the notification unit 505 notifies that there are no similar roads. The source of the inquiry and the notification destination of the notification unit 505 are not limited to the peculiar determination unit 51.

[0064] (Actions, Effects, etc.) According to the information providing device 500, information providing method, and program disclosed herein, feature data for a first road segment including a first road section and the surrounding area of ​​the first road section is compared with feature data for a second road segment including a second road section and the surrounding area of ​​the second road section to determine whether the first road segment and the second road segment are similar, thereby making it possible to obtain an appropriate determination result regarding the similarity of the roads.

[0065] In addition, according to the information providing device 500, information providing method, and program disclosed herein, a list of second road segments similar to the first road segment is created based on the results of the judgment, so that inquiries about similar roads can be answered without making a similarity judgment.

[0066] In the above embodiment, the use of GNSS has the advantage of eliminating the need for an in-vehicle camera, eliminating the need for image processing and the inability to capture images due to the surrounding environment, and enabling more accurate measurement of speed, etc. Therefore, the driving condition monitoring device and driving condition monitoring method disclosed herein can stably monitor the driving condition.

[0067] In addition, by aggregating the GNSS information at the central data device 1, it is possible to prevent tampering with location information, and it also has the advantage of being able to grasp the movements of vehicles around the vehicle and identify the origins of dangerous driving.

[0068] Furthermore, by utilizing this technology, it will be possible to provide a variety of services in addition to dangerous driving, such as evaluating driver ability, charging tolls on toll roads, and compiling traffic volume statistics.

[0069] (Other embodiments) Although the embodiment of the present disclosure has been described above in detail with reference to the drawings, the specific configuration is not limited to this embodiment, and design changes and the like within the scope of the gist of the present disclosure are also included. For example, the in-vehicle device 10 may obtain information indicating the steering angle, information indicating the brake pedal angle, and the like using a CAN (Controller Area Network) or the like, and may add information corresponding to the left-right acceleration and information indicating the presence or absence of sudden braking to the position information and transmit it to the central data device 1. In this case, the travel information generating unit 21 can include this additional information in the travel information. In addition, the conditions for the comparison target may be the same driving section, time period, and weather, as well as the same day of the week, or whether it is a weekday or a holiday.

[0070] In addition, in the above embodiment, the information providing device 500 is configured to store in the memory unit 506 a road segment list 510 in which similar road segments 600 are associated with each other for each road segment 600. However, instead of or in addition to the road segment list 510, a list in which similar road segments 601 are associated with each other for each road section 601 may be stored in the memory unit 506.

[0071] (Computer Configuration) FIG. 22 is a schematic block diagram illustrating a configuration of a computer according to at least one embodiment. The computer 90 comprises a processor 91, a main memory 92, a storage 93, and an interface 94. The above-mentioned central data device 1, driving information processing device 2, road toll determination device 3, traffic statistics information notification device 4, traffic violation determination device 5, road toll management device 6, traffic statistics information management device 7, traffic violation management device 8, in-vehicle device 10, and information providing device 500 are implemented in a computer 90. The operations of each of the above-mentioned processing units are stored in the storage 93 in the form of a program. The processor 91 reads the program from the storage 93, expands it in the main memory 92, and executes the above-mentioned processing according to the program. The processor 91 also secures a storage area in the main memory 92 corresponding to each of the above-mentioned storage units according to the program.

[0072] The program may be for realizing a part of the functions to be performed by the computer 90. For example, the program may be for realizing the functions by combining with other programs already stored in the storage or by combining with other programs implemented in other devices. In another embodiment, the computer may include a custom LSI (Large Scale Integrated Circuit) such as a PLD (Programmable Logic Device) in addition to or instead of the above configuration. Examples of PLDs include PAL (Programmable Array Logic), GAL (Generic Array Logic), CPLD (Complex Programmable Logic Device), and FPGA (Field Programmable Gate Array). In this case, some or all of the functions to be realized by the processor may be realized by the integrated circuit.

[0073] Examples of the storage 93 include a hard disk drive (HDD), a solid state drive (SSD), a magnetic disk, a magneto-optical disk, a compact disc read only memory (CD-ROM), a digital versatile disc read only memory (DVD-ROM), and a semiconductor memory. The storage 93 may be an internal medium directly connected to the bus of the computer 90, or an external medium connected to the computer 90 via an interface 94 or a communication line. In addition, when the program is distributed to the computer 90 via a communication line, the computer 90 that receives the program may load the program into the main memory 92 and execute the above-mentioned process. In at least one embodiment, the storage 93 is a non-transitory tangible storage medium.

[0074] <Additional Notes> The information providing device 500 described in the above embodiment can be understood, for example, as follows.

[0075] (1) The information providing device 500 of the first aspect includes an acquisition unit 501 that acquires feature data (road segment feature data 5091) including multiple types of information about a first road segment 600 including a first road section 601 and a surrounding area 602 of the first road section, and feature data including the multiple types of information about a second road segment 600 including a second road section 601 different from the first road section and a surrounding area 602 of the second road section, a determination unit 502 that compares the feature data for the first road segment 600 with the feature data for the second road segment 600 to determine whether the first road segment 600 and the second road segment 600 are similar, and a creation unit 503 that creates a list (road segment list 510) of the second road segment 600 that is similar to the first road segment 600 based on the result of the determination.

[0076] According to this and the following aspects, feature data for a first road segment including a first road segment and its surrounding area is compared with feature data for a second road segment including a second road segment and its surrounding area to determine whether the first road segment and the second road segment are similar, so that information about the surrounding area can be taken into consideration to obtain an appropriate determination result regarding the similarity of the roads. Also, a list of second road segments similar to the first road segment is created based on the result of the determination, so that an inquiry about roads similar to the first road segment can be answered without making a similarity determination.

[0077] (2) The information providing device 500 according to the second aspect is the information providing device 500 according to (1), and the feature data (road section feature data 5091) includes at least one of information on other roads connecting to the first and second road sections 600 (number of connected main roads 50934) or information corresponding to the ratio of urban areas in the surrounding area (habitable area ratio 50941, habitable area population density 50942, forest ratio 50943, wilderness ratio 50944). According to this aspect, for example, it is possible to determine similarity in consideration of features related to outflow from the target road to connecting roads or surrounding facilities, and inflow from connecting roads or surrounding facilities to the target road.

[0078] (3) The information providing device 500 according to a third aspect is the information providing device 500 according to (1), and the feature data includes information on other roads connecting to the first and second road sections (number of main road connections 50934) and information corresponding to the ratio of urban areas to the surrounding area (habitable area ratio 50941, habitable area population density 50942, forest ratio 50943, wilderness ratio 50944). According to this aspect, for example, it is possible to determine similarity in consideration of features related to outflow from the target road to connecting roads and surrounding facilities, and inflow from connecting roads to the target road and surrounding facilities.

[0079] (4) The information providing device 500 according to a fourth aspect is the information providing device 500 according to (3), in which the feature data (road section feature data 5091) further includes information corresponding to hourly traffic volume (hourly traffic volume 5095). According to this aspect, it is possible to determine the similarity in consideration of the feature related to the traffic volume.

[0080] (5) The information providing device 500 according to the fifth aspect is the information providing device 500 according to any one of (1) to (4), and further includes a notification unit 505 that, in response to an inquiry including information identifying the first road section 601, refers to the list (road section list 510) and notifies information corresponding to the similar second road section 600. [Explanation of symbols]

[0081] 500 Information provision device 100 Driving condition monitoring device 1 Central Data Unit 2. Driving information processing device 3 Road toll determination device 4 Traffic statistics information notification device 5 Traffic violation determination device 6 Road toll management device 7 Traffic statistics information management device 8 Traffic violation management device 10 Onboard equipment 21 Driving information generation unit 51 Singularity determination section 501 Acquisition Department 502 Judgment section 503 Creation Department 505 Notification Department 510 Road Segment List 600 Road Segments 601 Road Section 602, 603 Surrounding area 5091 Road segment characteristic data

Claims

1. an acquisition unit that acquires feature data including multiple types of information about a first road segment including a first road section and a surrounding area of ​​the first road section, and feature data including the multiple types of information about a second road segment including a second road section different from the first road section and a surrounding area of ​​the second road section; a determination unit that compares feature data for the first road segment with feature data for the second road segment to determine whether the first road segment and the second road segment are similar to each other; a creation unit that creates a list related to the second road segment similar to the first road segment based on a result of the determination; An information providing device comprising:

2. The characteristic data includes at least one of information related to other roads connected to the first and second road sections and information corresponding to a proportion of an urban area in the surrounding area.

2. The information providing device according to claim 1.

3. The characteristic data includes information on other roads connected to the first and second road sections and information corresponding to a proportion of an urban area in the surrounding area.

2. The information providing device according to claim 1.

4. The characteristic data further includes information corresponding to hourly traffic volume.

4. The information providing device according to claim 3.

5. a notification unit that, in response to an inquiry including information identifying the first road section, refers to the list and notifies information corresponding to the similar second road section; The information providing device according to claim 1 , further comprising:

6. An information providing device acquires feature data including multiple types of information about a first road section including a first road section and a surrounding area of ​​the first road section, and feature data including the multiple types of information about a second road section including a second road section different from the first road section and a surrounding area of ​​the second road section; a step in which the information providing device compares feature data for the first road segment with feature data for the second road segment to determine whether the first road segment and the second road segment are similar; creating a list of the second road segments similar to the first road segment based on a result of the determination by the information providing device; Methods of providing information, including:

7. acquiring feature data including multiple types of information about a first road segment including a first road segment and a surrounding area of ​​the first road segment, and acquiring feature data including the multiple types of information about a second road segment including a second road segment different from the first road segment and a surrounding area of ​​the second road segment; comparing feature data for the first road segment with feature data for the second road segment to determine whether the first road segment and the second road segment are similar; generating a list of the second road segments that are similar to the first road segment based on the result of the determination; A program that causes a computer to execute the following.

Images