JP6971830B2 - Road traffic control device and self-driving vehicle control method - Google Patents

Description

Embodiments of the present invention relate to a road traffic control device and an autonomous driving vehicle control method.

On roads for vehicles, road conditions including traffic conditions (traffic volume, occupancy rate, average speed, etc.), weather conditions (sunny, cloudy, rain, snow, wind, etc.), road surface conditions (dry, wet, frozen, etc.), etc. The incidence of traffic accidents varies depending on the type.

Conventionally, among multiple sections of a road, for sections where the incidence of traffic accidents is statistically high, for example, traffic accident prevention is provided on the roadside of that section or on the information board provided above the road. By displaying a warning about traffic accidents, it is taken as a countermeasure against traffic accidents.

Japanese Unexamined Patent Publication No. 10-334391 Japanese Unexamined Patent Publication No. 2017-126892 Japanese Unexamined Patent Publication No. 2010-66827 Japanese Unexamined Patent Publication No. 2006-298006 Japanese Unexamined Patent Publication No. 2014-35639

However, in the above-mentioned conventional technology, the driver who sees the warning display on the information board only takes an action to prevent a traffic accident of his / her own vehicle, and the road condition in which the occurrence rate of a traffic accident is high in the entire section. Was not trying to improve.

Therefore, an object of the present invention is to improve a road condition in which the incidence of traffic accidents is high.

The road traffic control device of the embodiment includes a creation unit, an acquisition unit, an estimation unit, a calculation unit, and a communication control unit. For each of the multiple sections of the road, the creation department used at least information on past traffic conditions and past accident data, and associated the degree of accident occurrence, which indicates the likelihood of an accident, for each of the multiple road conditions. Create accident occurrence degree information. The acquisition unit acquires information on the current traffic conditions for each of the plurality of sections. The estimation unit estimates the current accident occurrence degree for each of the plurality of sections based on the accident occurrence degree information and the current traffic condition information. When there is a section in which the accident occurrence degree estimated by the estimation unit is higher than a predetermined threshold value, the calculation unit reduces the accident occurrence degree in the section to the predetermined threshold value or less. The driving control information for driving the self-driving vehicle existing in is calculated. The communication control unit transmits the travel control information calculated by the calculation unit to the autonomous driving vehicle.

FIG. 1 is an overall configuration diagram of the road traffic control system of the embodiment. FIG. 2 is a block diagram of the road traffic control device of the embodiment. FIG. 3 is a block diagram of the accident occurrence degree table of the embodiment. FIG. 4 is a flowchart showing a process of creating an accident occurrence degree table according to the embodiment. FIG. 5 is a flowchart showing a transmission process of travel control information according to the embodiment.

Hereinafter, the road traffic control device of the embodiment and the autonomous driving vehicle control method will be described with reference to the attached drawings. In the following embodiment, the case of a highway as a road for vehicles will be described as an example. FIG. 1 is an overall configuration diagram of the road traffic control system 1000 of the embodiment. FIG. 2 is a block diagram of the road traffic control device 1 of the embodiment.

The road traffic control system 1000 includes a road traffic control device 1, a traffic counter 21, a roadside control device 22, a camera 31, a roadside control device 32, a meteorological observation device 4, and a probe information system 6.

On the road R (expressway), the manually driven vehicle C1 and the automatically driven vehicle C2 travel. The manually driven vehicle C1 is a conventional vehicle that travels by various driving operations by the driver. The self-driving vehicle C2 is a vehicle that travels on behalf of all or part of various driving operations by the driver. More specifically, the self-driving vehicle C2 recognizes the surrounding environment by radar, LIDAR (Laser Imaging Detection And Ranging), GPS (Global Positioning System), camera, etc., and autonomously heads toward the specified destination. Run. Hereinafter, when the manual driving vehicle C1 and the automatic driving vehicle C2 are not distinguished, they are simply referred to as "vehicles".

The meteorological observation device 4 observes the weather and transmits the meteorological observation data to the road traffic control device 1. The meteorological observation device 4 includes, for example, a temperature sensor, a humidity sensor, a wind direction anemometer, a pyranometer, a rain gauge, an atmospheric pressure sensor, and the like.

The traffic counter 21 is provided for each of the plurality of lanes on the road R. Each traffic counter 21 is a vehicle detector that detects a vehicle traveling on the lane. The traffic counter 21 is embedded, for example, under the road R and is realized by a rectangular loop coil that senses the vehicle by utilizing the fact that the inductance changes with the approach of the vehicle. The roadside control device 22 transmits the vehicle number data, which is the data of the number of vehicles detected by the traffic counter 21, to the road traffic control device 1. At least one set of the traffic counter 21 and the roadside control device 22 is installed between each interchange.

The camera 31 photographs a vehicle on the road R. The roadside control device 32 transmits image data, which is image data taken by the camera 31, to the road traffic control device 1.

A road surface condition sensor 5 is provided on the tire portion of the autonomous driving vehicle C2. The road surface condition sensor 5 senses the road surface condition (details will be described later). The self-driving vehicle C2 wirelessly transmits the sensing result of the road surface condition by the road surface condition sensor 5 to the road traffic control device 1.

The road traffic control device 1 is, for example, a computer system generally called a road traffic control system. Although the road traffic control device 1 is shown as one computer device in FIG. 2 for the sake of brevity, it may be realized by a plurality of computer devices. The road traffic control device 1 includes a processing unit 11, a storage unit 12, a display unit 13, an input unit 14, and a communication unit 15.

The storage unit 12 is a storage device such as an HDD (Hard Disk Drive) or SSD (Solid State Drive). The storage unit 12 includes road information, a probe information database, past road condition information for each section (for each of a plurality of sections on the road R), current road condition information for each section, and an accident occurrence degree table for each section (for each section). Accident occurrence degree information) etc. are memorized. The section is set by the user, and is, for example, between each interchange.

The road information is information related to the road R, for example, position information, identification information, etc. related to each of a plurality of sections in which the road R is divided.

The probe information database is a database that collects probe information acquired from an external probe information system 6. Here, the probe information refers to information such as the position, speed, and acceleration of the vehicle transmitted by the probe car, information on weather conditions, and information on road surface conditions. Further, the probe car refers to a vehicle having such an information transmission function.

Information on past road conditions is collected for each section. The past road condition information includes past traffic condition information, past weather condition information, and past road surface condition information.

Information on past traffic conditions includes, for example, information such as traffic volume [vehicles / h], occupancy rate [%] (or vehicle density [vehicles / km]), and average speed [km / h]. Information on past weather conditions is, for example, information on sunny weather, cloudy weather, rain, snow, wind, and the like. Information on past road surface conditions includes, for example, dry, semi-wet, wet, snow, compressed snow (snow is squeezed by vehicle tires), freezing, sherbet (snow partially melted sherbet), etc. Is.

Information on current road conditions (for example, for the last few minutes) is collected for each section. The current road condition information includes current traffic condition information, current weather condition information, and current road surface condition information (the content of each information is the same as the corresponding past information described above).

Each probe information in the probe information database is also sorted and used as information on past road conditions and information on current road conditions for each section. Further, the road traffic control device 1 uses the sensing result of the road surface condition received from the autonomous driving vehicle C2 as information on the past road surface condition and information on the current road surface condition for each section. Further, the road traffic control device 1 uses the meteorological observation data received from the meteorological observation device 4 as information on the past weather condition and information on the current weather condition for each section.

Past accident data is collected (accumulated) for each section. The past accident data includes the accident occurrence point, the accident occurrence date and time, the accident type, etc. as information on the accident. The past accident data is preferably, but is not limited to, accident information for the past several years or more.

The accident occurrence degree table is created for each section. The accident occurrence degree table is created by the creation unit 111 using information on past road conditions, and is information associated with the accident occurrence degree indicating the likelihood of an accident for each of a plurality of road conditions (details are as follows). Later).

The processing unit 11 controls the overall operation of the road traffic control device 1 and realizes various functions of the road traffic control device 1. The processing unit 11 includes, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory). The CPU comprehensively controls the operation of the road traffic control device 1. ROM is a storage medium for storing various programs and data. RAM is a storage medium for temporarily storing various programs and rewriting various data. The CPU executes a program stored in the ROM, the storage unit 12, etc. using the RAM as a work area (work area).

The processing unit 11 includes a creation unit 111, an acquisition unit 112, an estimation unit 113, a calculation unit 114, a communication control unit 115, and a display control unit 116. Each unit 111-116 is realized, for example, by the CPU executing a program, but a part or all thereof may be realized by hardware. In the following, when a plurality of processes among the respective units 111 to 116 in the processing unit 11 are described, and when a process other than the respective units 111 to 116 is described, the operating subject is referred to as a “processing unit 11”.

The creation unit 111 indicates the likelihood of an accident for each of a plurality of road conditions by using information on past traffic conditions and information on past road conditions including past accident data for each of a plurality of sections on the road R. Create an accident occurrence degree table (accident occurrence degree information) associated with the accident occurrence degree. For example, the creation unit 111 uses the past accident data, the past traffic condition information, the past weather condition information, and the past road surface condition information stored in the storage unit 12 for a predetermined section on the road R. , The accident occurrence pattern is learned based on a predetermined learning algorithm (for example, a learning algorithm using a self-organizing map), and an accident occurrence degree table showing the accident occurrence degree for each of a plurality of road conditions is created.

Here, the self-organizing map is a kind of neural network applied to important fields in the real world such as process analysis, control, search system, and information analysis for management, and is a kind of high-dimensional input data. Is an algorithm for clustering without any preliminary information such as a teacher signal (output that is considered ideal for input data). Since the self-organizing map is disclosed in, for example, Japanese Patent Application Laid-Open No. 2014-35639, further description thereof will be omitted here.

Here, FIG. 3 is a configuration diagram of an accident occurrence degree table of the embodiment. In the accident occurrence degree table, traffic conditions (traffic volume, occupancy rate, average speed), weather conditions, road surface conditions, accident occurrence degree for each unit number (identification number of competing layer in learning algorithm using self-organizing map) Are associated with each other. That is, in the accident occurrence degree table, the accident occurrence degree indicating the susceptibility to an accident is associated with each of a plurality of road conditions (traffic condition, weather condition, road surface condition). For example, the numerical value of the weather condition is set to a larger value as the degree of the factor that induces a traffic accident such as rain, snow, and wind is larger. Further, for example, the numerical value of the road surface condition is set to a larger value as the degree of factors that induce a traffic accident such as the degree of wetness and the degree of freezing is larger.

Returning to FIG. 2, the acquisition unit 112 acquires information on the current road condition and information on the past road condition for each of the plurality of sections of the road R, and stores the acquired information in the storage unit 12. For example, the acquisition unit 112 acquires meteorological observation data from the meteorological observation device 4. Further, the acquisition unit 112 acquires vehicle number data from the roadside control device 22. Further, the acquisition unit 112 acquires image data from the roadside control device 32. The acquired image data is analyzed by the processing unit 11 and can be used as vehicle number data, meteorological observation data, and road surface condition data. Further, the acquisition unit 112 acquires the sensing result of the road surface condition from the autonomous driving vehicle C2.

The estimation unit 113 estimates the current accident occurrence degree for each of the plurality of sections of the road R based on the accident occurrence degree table and the information of the current road condition.

The calculation unit 114 exists in the section so that when there is a section in which the accident occurrence degree estimated by the estimation unit 113 is higher than the predetermined threshold value, the accident occurrence degree in the section is reduced to the predetermined threshold value or less. The travel control information for driving the autonomous driving vehicle C2 is calculated. The case where the accident occurrence degree is higher than the predetermined threshold value is the case where at least one of the traffic condition, the weather condition, and the road surface condition is bad. In that case, the degree of accident occurrence is reduced by controlling the autonomous driving vehicle C2 to change the traffic condition.

For example, if the average speed of the section is reduced to a predetermined speed (for example, a speed 10 km / h slower than the current average speed) and the accident occurrence rate of the section is reduced to a predetermined threshold or less, the travel control information is automatically set. This is information for driving the driving vehicle C2 at its predetermined speed.

Further, for example, if the average speed of the section is lowered to a predetermined speed (for example, a speed 10 km / h slower than the current average speed), the degree of accident occurrence in the section is reduced to a predetermined threshold or less, and the operation is automatic. When it is particularly effective to drive the driving vehicle C2 in a predetermined lane (for example, an overtaking lane) at a predetermined speed, the travel control information indicates that the automatic driving vehicle C2 travels in the predetermined lane at the predetermined speed. It is information to make you.

Further, for example, when the accident occurrence degree of the section is reduced to a predetermined threshold value or less by reducing the traffic volume and the occupancy rate of the section to a predetermined degree, the traveling control information is used for the autonomous driving vehicle C2 on a predetermined route (for example,). , Information for driving in the nearest service area or parking area).

The communication control unit 115 controls communication by the communication unit 15 with the weather observation device 4, the roadside control device 22, the roadside control device 32, the autonomous driving vehicle C2, and the like. The communication control unit 115 wirelessly communicates with the autonomous driving vehicle C2. Further, the communication control unit 115 transmits the travel control information calculated by the calculation unit 114 to the autonomous driving vehicle C2.

The display control unit 116 controls to display various information on the display unit 13.

The display unit 13 is a means for displaying information, and is, for example, a liquid crystal display device (LCD (Liquid Crystal Display)), an organic EL (Electro-Luminescence) display device, or the like. The input unit 14 is a means for receiving a user's operation, and is, for example, a keyboard, a mouse, or the like. The communication unit 15 is a communication interface for communicating with an external device.

Next, the process of creating the accident occurrence degree table of the embodiment will be described with reference to FIG. FIG. 4 is a flowchart showing a process of creating an accident occurrence degree table according to the embodiment. Here, the processing relating to one of the plurality of sections on the road R will be described.

First, in step S1, the creating unit 111 refers to the storage unit 12 and refers to past road condition information (past accident data, past traffic condition information, past weather condition information, past road surface) regarding the target section. Status information) is read.

Next, in step S2, the creating unit 111 correlates the past road condition with the likelihood of an accident (accident occurrence degree) for the target section by using the information of the past road condition read in step S1. Learn relationships. As a learning method, for example, a method using a self-organizing map as described above is used.

Then, in step S3, the creating unit 111 creates an accident occurrence degree table based on the result learned in step S2. For example, the creation unit 111 creates a table of the correlations obtained by the self-organizing map, and creates an accident occurrence degree table in which the accident occurrence degree is associated with each of a plurality of road conditions.

In this way, the creating unit 111 can create the accident occurrence degree table. Further, the creating unit 111 creates such an accident occurrence degree table for each section. The creation unit 111 updates the accident occurrence degree table by using the information of the past road conditions acquired at that time, for example, at a predetermined timing or when an instruction is input by the user. The predetermined timing is, for example, the timing at which the information on the past road conditions for the latest one year is accumulated every year. In addition, when an instruction is input by the user, for example, when a large road is created around the target road and the flow of vehicles on the target road changes, then, for example, for several months, etc. This is a case where the user inputs an instruction for updating the accident occurrence degree table by using the input unit 14 of the road traffic control device 1 when a sufficient amount of past road condition information is accumulated.

Next, the transmission process of the traveling control information of the embodiment will be described with reference to FIG. FIG. 5 is a flowchart showing a transmission process of travel control information according to the embodiment.

First, in step S11, the processing unit 11 determines whether or not the processing timing has arrived, and if Yes, the process proceeds to step S12, and if No, the process returns to step S11. The processing timing may be, for example, every 5 minutes, but the processing timing is not limited to this.

Next, the processing unit 11 performs processing for each section in steps S12 to S18. That is, the processing unit 11 first performs the processing of steps S13 to S17 for the first section, then performs the processing of steps S13 to S17 for the second section, and so on. Do about.

In step S13, the estimation unit 113 reads out the current road condition information from the storage unit 12 for the target section.

Next, in step S14, the estimation unit 113 estimates the current accident occurrence degree based on the accident occurrence degree table corresponding to the section and the information of the current road condition.

Next, in step S15, the calculation unit 114 determines whether or not the accident occurrence degree estimated in step S14 is higher than a predetermined threshold value, proceeds to step S16 in the case of Yes, and in step S18 in the case of No. Proceed to.

In step S16, the calculation unit 114 calculates travel control information for driving the autonomous driving vehicle C2 existing in the section so that the accident occurrence degree in the section is reduced to a predetermined threshold value or less.

Next, in step S17, the communication control unit 115 transmits the travel control information calculated in step S16 to the autonomous driving vehicle C2. For example, when the driving control information is information for driving the autonomous driving vehicle C2 at a speed 10 km / h slower than the current average speed of the section, the autonomous driving vehicle C2 that has received the driving control information is automatically driven. It runs at that speed by driving. Then, the following vehicle will also be affected by this, and the average speed of that section will decrease. Therefore, the average speed can be reduced for the entire section, and the road condition with a high incidence of traffic accidents can be improved.

Further, for example, when the traveling control information is information for driving the autonomous driving vehicle C2 in the overtaking lane at a predetermined speed, the autonomous driving vehicle C2 that has received the driving control information is in the overtaking lane by automatic driving. Drive at that speed. Then, the following vehicle in the overtaking lane, which tends to travel at high speed, is also affected by the influence, and the average speed of the overtaking lane in the section decreases, and eventually the average speed of the entire section also decreases. Therefore, it is possible to improve the road condition in which the incidence of traffic accidents is high for the entire section.

Further, for example, when the driving control information is information for driving the autonomous driving vehicle C2 on a route entering the nearest service area or parking area, the autonomous driving vehicle C2 that has received the driving control information is automatically driven. Enter the nearest service area or parking area. Then, the traffic volume and the occupancy rate of the section will gradually decrease. Therefore, it is possible to improve the road condition in which the incidence of traffic accidents is high for that section. After step S17, the process proceeds to step S18.

In this way, according to the road traffic control device 1 of the present embodiment, it is possible to improve the road condition in which the incidence of traffic accidents is high. That is, the road traffic control device 1 estimates the current accident occurrence degree based on the accident occurrence degree table in which the accident occurrence degree is associated with each of a plurality of road conditions and the information of the current road condition, and the accident occurrence degree is estimated. Calculates driving control information for driving the autonomous driving vehicle C2 existing in the section so that the degree of accident occurrence in the section is reduced to the predetermined threshold or less when there is a section higher than the predetermined threshold. .. Then, the road traffic control device 1 transmits the travel control information to the autonomous driving vehicle C2, and causes the autonomous driving vehicle C2 to reduce the speed, change lanes, change the traveling route, and the like, thereby causing a traffic accident in the entire section. Can reduce the incidence of.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.

For example, in the above-described embodiment, regarding the creation of the accident occurrence degree table and the processing using the accident occurrence degree table, traffic condition information, weather condition information, and road surface condition information are used as road condition information. However, weather condition information and road surface condition information are not essential and need not be used.

Further, when the driving control information is the driving route change information, the changed route is not limited to the route entering the nearest service area or parking area described above, and is different from the original route to the destination. There may be.

Further, the means for measuring the traffic volume in each section is not limited to the above-mentioned traffic counter 21, and may be another means such as the above-mentioned camera 31 or an ultrasonic sensor for measuring the passage of a vehicle.

Further, in the above-described embodiment, a method using a self-organizing map is exemplified as a method for performing learning and the like. However, as a method for learning or the like, various methods other than the method using the self-organizing map can be considered. For example, as a relatively simple method, a method of retaining (accumulating) information on past road conditions at the time of an accident and simply comparing it with information on current road conditions, or information on past road conditions at the time of an accident Is clustered by statistical processing to generate information on road conditions in similar cases when an accident occurs. Further, as another method, a method using other multivariate analysis such as a pagean network can be considered.

Further, in the above-described embodiment, the case of a highway as the road R is taken as an example, but the present invention is not limited to this, and any road other than the highway can be targeted.

Further, the program executed by the road traffic control device 1 of the present embodiment is a file in an installable format or an executable format, and is a CD (Compact Disc) -ROM, a flexible disc (FD), or a CD-R (Recordable). , DVD (Digital Versatile Disk) and the like, and can be recorded and provided on a recording medium readable by a computer device. Further, the program executed by the road traffic control device 1 of the present embodiment may be provided or distributed via a network such as the Internet.

1 ... Road traffic control device, 4 ... Meteorological observation device, 5 ... Road surface condition sensor, 6 ... Probe information system, 11 ... Processing unit, 12 ... Storage unit, 13 ... Display unit, 14 ... Input unit, 15 ... Communication unit, 21 ... Traffic counter, 22 ... Roadside control device, 31 ... Camera, 32 ... Roadside control device, 111 ... Creation unit, 112 ... Acquisition unit, 113 ... Estimate unit, 114 ... Calculation unit, 115 ... Communication control unit, 116 ... Display Control unit, C1 ... manual driving vehicle, C2 ... automatic driving vehicle, R ... road.

Claims (12)

Accident occurrence degree information that indicates the likelihood of an accident for each of multiple road conditions, using at least past traffic condition information and past accident data for each of multiple sections of the road. And the creation department to create
An acquisition unit that acquires information on the current traffic conditions for each of the plurality of sections,
An estimation unit that estimates the current accident occurrence degree based on the accident occurrence degree information and the current traffic condition information for each of the plurality of sections.
When there is a section in which the accident occurrence degree estimated by the estimation unit is higher than a predetermined threshold value, the automatic operation existing in the section so that the accident occurrence degree in the section is reduced to the predetermined threshold value or less. A calculation unit that calculates driving control information for driving a driving vehicle,
A communication control unit that transmits the travel control information calculated by the calculation unit to the autonomous driving vehicle, and a communication control unit.
Road traffic control device equipped with. The creating unit creates the accident occurrence degree information for each of the plurality of sections by further using the information of the past weather conditions.
The acquisition unit further acquires information on the current weather conditions for each of the plurality of sections.
The road traffic control device according to claim 1, wherein the estimation unit estimates the current accident occurrence degree for each of the plurality of sections based on the information on the current weather conditions. The creating unit creates the accident occurrence degree information for each of the plurality of sections by further using the information of the past road surface condition.
The acquisition unit further acquires information on the current road surface condition for each of the plurality of sections, and obtains information on the current road surface condition.
The road traffic control device according to claim 1 or 2, wherein the estimation unit estimates the current accident occurrence degree for each of the plurality of sections based on the information on the current road surface condition. The road traffic control device according to any one of claims 1 to 3, wherein the traveling control information is information for driving the autonomous driving vehicle at a predetermined speed. The road traffic control device according to any one of claims 1 to 3, wherein the traveling control information is information for driving the autonomous driving vehicle in a predetermined lane at a predetermined speed. The road traffic control device according to any one of claims 1 to 3, wherein the travel control information is information for driving the autonomous driving vehicle on a predetermined route. Accident occurrence degree information that indicates the likelihood of an accident for each of multiple road conditions, using at least past traffic condition information and past accident data for each of multiple sections of the road. And the creation steps to create
An acquisition step for acquiring information on the current traffic conditions for each of the plurality of sections, and
For each of the plurality of sections, an estimation step for estimating the current accident occurrence degree based on the accident occurrence degree information and the current traffic condition information, and an estimation step.
When there is a section in which the accident occurrence degree estimated by the estimation step is higher than a predetermined threshold value, the automatic operation existing in the section so that the accident occurrence degree in the section is reduced to the predetermined threshold value or less. A calculation step for calculating driving control information for driving a driving vehicle, and
A communication control step for transmitting the travel control information calculated by the calculation step to the autonomous driving vehicle, and a communication control step.
Self-driving vehicle control method. In the creation step, the accident occurrence degree information is created for each of the plurality of sections by using the information of the past weather conditions.
The acquisition step further acquires information on the current weather conditions for each of the plurality of sections.
The autonomous driving vehicle control method according to claim 7, wherein the estimation step estimates the current accident occurrence degree for each of the plurality of sections and based on the information on the current weather conditions. In the creation step, the accident occurrence degree information is created for each of the plurality of sections by using the information of the past road surface condition.
The acquisition step further acquires information on the current road surface condition for each of the plurality of sections.
The autonomous driving vehicle control method according to claim 7 or 8, wherein the estimation step estimates the current accident occurrence degree for each of the plurality of sections and based on the information on the current road surface condition. The automatic driving vehicle control method according to any one of claims 7 to 9, wherein the traveling control information is information for driving the automatic driving vehicle at a predetermined speed. The autonomous driving vehicle control method according to any one of claims 7 to 9, wherein the traveling control information is information for driving the autonomous driving vehicle in a predetermined lane at a predetermined speed. The automatic driving vehicle control method according to any one of claims 7 to 9, wherein the traveling control information is information for driving the automatic driving vehicle on a predetermined route.

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