JP7239815B2 - Traffic congestion prevention server, traffic congestion prevention device, traffic congestion prevention system, traffic congestion prevention method, and program - Google Patents

Description

The present invention relates to a traffic congestion prevention server, a traffic congestion prevention device, a traffic congestion prevention system, a traffic congestion prevention method, and a program.

Techniques for providing information on traffic congestion occurring on roads and information on detour routes for avoiding traffic congestion are known.

For example, Patent Literature 1 describes a technique of acquiring various types of information including destination information from each traveling vehicle and notifying each vehicle of a detour route to the destination. Japanese Patent Laid-Open No. 2002-201002 describes a technique of acquiring driving state information of a vehicle and transmitting notification information when the vehicle is the leading vehicle in a traffic jam.

JP 2017-134476 A JP 2011-133998 A

In Patent Literature 1 and Patent Literature 2, information for resolving congestion is individually transmitted to each vehicle. In order to suppress the occurrence of traffic jams, it is effective to analyze the points where traffic jams occurred and the behavior of vehicles that trigger traffic congestion, and provide information to vehicles that are likely to be triggers. It is considered to be a target.

The present invention proposes a traffic congestion prevention server, a traffic congestion prevention device, a traffic congestion prevention system, a traffic congestion prevention method, and a program capable of suppressing the occurrence of traffic congestion.

A traffic congestion prevention server of the present invention includes a road condition search unit that searches a road condition history database for road conditions under at least one second condition similar to a designated first condition, , a congestion situation estimation unit for estimating a congestion occurrence point and a congestion occurrence factor under the first condition; a vehicle traveling information receiving section for receiving travel information of each vehicle traveling under the first condition; and the congestion occurrence factor; , a trigger vehicle extraction unit for extracting a trigger vehicle that may trigger traffic congestion from the travel information; and a transmission unit for transmitting driving support information as a notification signal to the trigger vehicle.

A traffic jam prevention device of the present invention includes a road condition search unit that searches a road condition history database for each road condition under at least one second condition similar to a specified first condition; Based on a plurality of road conditions, a congestion situation estimation unit for estimating a congestion occurrence point and a congestion occurrence factor under the first condition, and an own vehicle travel information acquisition unit for acquiring travel information of a vehicle traveling under the first condition. a trigger vehicle extracting unit for extracting a trigger vehicle that may serve as a trigger for generating traffic congestion from the traffic congestion cause and the travel information; and transmitting driving support information as a notification signal to the trigger vehicle. and a transmitter.

A traffic jam prevention system of the present invention comprises a traffic jam prevention server of the present invention and a vehicle device. The vehicle device includes a vehicle travel information transmission unit that acquires vehicle travel information and transmits the information to the congestion prevention server, and a reception unit that receives the driving support information from the congestion prevention server as a notification signal.

The congestion prevention method of the present invention includes a road condition search step of searching a road condition history database for road conditions under at least one second condition similar to a specified first condition; , a congestion situation estimation step of estimating a congestion occurrence point and a congestion occurrence factor under the first condition; a vehicle traveling information receiving step of receiving travel information of each vehicle traveling under the first condition; and the congestion occurrence factor; a trigger vehicle extracting step of extracting a trigger vehicle that may trigger a traffic jam from the travel information; and a notification transmission step of transmitting driving support information as a notification signal to the trigger vehicle. .

The program of the present invention includes a road condition search step of searching a road condition history database for road conditions under at least one second condition similar to a designated first condition, and based on the road conditions under the second conditions, a congestion situation estimation step of estimating a congestion occurrence point and a congestion occurrence factor under the first condition; a vehicle traveling information receiving step of receiving travel information of each vehicle traveling under the first condition; the congestion occurrence factor; A trigger vehicle extracting step of extracting a trigger vehicle that may trigger a traffic jam from travel information, and a transmitting step of transmitting driving support information as a notification signal to the trigger vehicle as a congestion prevention server. Run it on a working computer.

ADVANTAGE OF THE INVENTION According to this invention, generation|occurrence|production of a traffic jam can be suppressed.

FIG. 1 is a schematic diagram showing an example of the configuration of the congestion prevention system according to the first embodiment. FIG. 2 is a block diagram showing an example of the configuration of the congestion prevention server according to the first embodiment. FIG. 3 is a block diagram showing an example of the configuration of the vehicle-mounted device according to the first embodiment. FIG. 4A is a schematic diagram for explaining the road condition history database. FIG. 4B is a schematic diagram for explaining the road condition history database. FIG. 5 is a flow chart showing an example of the processing flow of the congestion prevention server according to the first embodiment. FIG. 6 is a block diagram showing an example of the configuration of the vehicle-mounted device according to the second embodiment. FIG. 7 is a flow chart showing an example of the processing flow of the vehicle-mounted device according to the second embodiment.

DETAILED DESCRIPTION OF THE INVENTION Embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the present invention is not limited by this embodiment, and when there are a plurality of embodiments, a combination of each embodiment is also included.

[First Embodiment]
A congestion prevention system according to the present embodiment will be described with reference to FIGS. 1, 2, and 3. FIG. FIG. 1 is a schematic diagram showing an example of the configuration of a congestion prevention system according to this embodiment. FIG. 2 is a block diagram showing an example of the configuration of the congestion prevention server according to this embodiment. FIG. 3 is a block diagram showing an example of the vehicle-mounted device according to this embodiment.

The congestion prevention system 1 includes a congestion prevention server 100 and an on-vehicle device 200 mounted on each vehicle V. FIG. In other words, the congestion prevention system 1 includes a congestion prevention server 100 and a plurality of vehicle-mounted devices 200 . The congestion prevention server 100 and the plurality of vehicle-mounted devices 200 are communicably connected via a network 300 .

As shown in FIG. 2, the congestion prevention server 100 includes a storage unit 110, a control unit 120, and a communication unit . The traffic congestion prevention server 100 determines the cause of the traffic congestion among the vehicles V based on the past road conditions, the current road conditions, and the information about the travel of each vehicle V acquired from the plurality of vehicle-mounted devices 200. Driving support information for preventing the occurrence of traffic congestion is notified to the predicted trigger vehicle.

The storage unit 110, for example, the control unit 120, stores a program for realizing the function of each unit. In this case, the control unit 120 expands and executes the programs stored in the storage unit 110 to implement the functions of the respective units. The storage unit 110 can be realized by, for example, semiconductor memory elements such as RAM (Random Access Memory), ROM (Read Only Memory), and flash memory, or storage devices such as hard disks, solid state drives, and optical disks. can be done.

The storage unit 110 stores a road condition history database 111 and a map database 112 .

The road condition history database 111 stores information including traffic volume indicating the degree of congestion at a predetermined point and road conditions associated with various conditions. In other words, in this embodiment, road conditions include traffic volume and various conditions.

Information stored in the road condition history database 111 will be described with reference to FIGS. 4A and 4B. 4A and 4B are schematic diagrams showing an example of information stored in the road condition history database 111. FIG.

As shown in FIGS. 4A and 4B, the road condition history database 111 stores information that associates locations, dates, weather, temperatures, traffic volumes, and event information. In FIG. 4A, the traffic volume indicates the degree of congestion at that location, and the degree of congestion is represented by integers from 1 to 5, for example. In this case, for example, the degree of congestion is the smallest when the value is 1, and the degree of congestion is the highest when the value is 5. As for the event information, event information being held around the location represents event information. The road condition history database 111 also stores information that associates the place where the traffic jam occurred, the date and time of the occurrence, and the cause of the occurrence as shown in FIG. 4B. Additional information such as a sag, a slope, or a confluence may be stored with respect to the place where the traffic jam occurred. The cause of the traffic congestion is identified from, for example, the image taken by the installed roadside camera or the image taken by the in-vehicle camera of each vehicle.

In the example shown in FIG. 4A , for example, at “A point” at “11:32 on January 6, 2018”, the weather is “cloudy”, the temperature is “6.3° C.”, the traffic volume is “2”, and the surrounding Events held in are associated with "none". The weather at "Point B" on "August 10, 2018 21:48" is "clear", the temperature is "24.2 ℃", the traffic volume is "5", and the event being held in the surrounding area is "Fireworks It is associated with being a "tournament". 4A and 4B show road conditions only at points A and B, the road condition history database 111 stores various conditions in association with each other. In addition, the road condition history database 111 may store various other conditions in association with each other. Various other conditions may be conditions that can affect the traffic volume.

Various types of map information are stored in the map database 112 . Map information includes information about roads that can affect traffic volume. The map information includes various information about roads, such as information about gradients of roads and information about sag portions existing on roads.

In this embodiment, the road condition history database 111 and the map database 112 are described as being stored in the storage unit 110, but this is an example and does not limit the present invention. The road condition history database 111 and the map database 112 may be stored in another server device or the like that is different from the congestion prevention server 100 .

The control unit 120 includes a condition acquisition unit 121, a road condition search unit 122, a traffic jam condition estimation unit 123, a vehicle travel information reception unit 124, a trigger vehicle extraction unit 125, a proposal unit 126, and a communication control unit 127. Prepare. The control unit 120 expands and executes the programs stored in the storage unit 110 to implement the functions of each unit. The control unit 120 can be implemented by, for example, an electronic circuit including a CPU (Central Processing Unit).

The condition acquisition unit 121 acquires various conditions regarding road conditions. The condition acquisition unit 121 acquires current road conditions at a predetermined point from an external server device or the like via the reception unit 131 of the communication unit 130 . The condition acquisition unit 121 may, for example, acquire road conditions from a roadside device such as an ITS (Intelligent Transport System) placed on the road. The condition acquisition unit 121 acquires, as road conditions, information about a location, date and time, traffic volume including the degree of congestion, weather, temperature, and events being held in the surrounding area at a predetermined time at a predetermined location. The predetermined point in time is, for example, the current date and time or a specified date and time. The condition acquired by the condition acquisition unit 121 is also called a first condition. The condition acquisition unit 121 outputs the acquired information to the road condition search unit 122 .

The road condition search unit 122 searches past road conditions from the road condition history database 111 based on the information input from the condition acquisition unit 121 . The road condition search unit 122 searches the road condition history database 111 for a plurality of second condition road conditions that at least partially match or have similarities to the road conditions of the first condition. The road condition search unit 122 outputs the search result to the congestion condition estimation unit 123 . Similarities are similarities regarding date and time, such as being on the same day of the previous year or on the same day of the week in the previous week with respect to the first condition, for example. It may be the first day of a long holiday, or the first weekend of the same month of the previous year, and is not limited to the same day or the same day of the week. Having similarities may mean, for example, that the traffic volume is increased or decreased by 5% or less compared to the first condition, or that the weather or temperature is considered to be substantially the same as compared to the first condition. The similarity may be, for example, the date of a major event.

The congestion condition estimation unit 123 analyzes the search result searched by the road condition search unit 122 . The congestion state estimation unit 123 calculates similarities between the first condition and the plurality of second conditions based on the search results. The congestion state estimation unit 123 calculates a weighting factor according to the degree of similarity between the first condition and the plurality of second conditions. The weighting coefficients are optimized by repeatedly learning the correlation between traffic volume and various conditions using a machine learning method such as deep learning using a neural network. For example, if it is determined that there is a strong correlation between the traffic volume and the day of the week and time period, the weighting factors for the day of the week and time period are increased. For example, machine learning may determine that there is a strong correlation between weather and traffic on weekend highways. In this way, the traffic congestion estimation unit 123 optimizes the weighting coefficients of the second condition, which is highly similar to the first condition, by learning, and estimates the traffic volume under the first condition. Specifically, the traffic congestion estimation unit 123 estimates the location and time at which congestion is likely to occur based on the location and time of congestion in the road conditions of the second condition that are highly similar to the current time. . The traffic congestion estimation unit 123 estimates the factors that are likely to trigger congestion based on the factors that trigger congestion in the road conditions of the second condition that are highly similar to the current time. For example, the congestion state estimation unit 123 estimates that a decrease in speed at a sag portion is a trigger for the occurrence of congestion. For example, the congestion state estimation unit 123 estimates that a sudden brake caused by an interruption is the trigger for the occurrence of traffic congestion. The congestion state estimation unit 123 may be configured integrally with the road state search unit 122 .

The traffic jam condition estimation unit 123 further estimates possible traffic congestion conditions based on the search results obtained by the road condition search unit 122 . The traffic jam state estimation unit 123 estimates the congestion occurrence point at the present time, which is the first condition, and the behavior of the vehicle that triggers the occurrence of traffic congestion at the congestion occurrence point. Specifically, the road conditions of the second condition that are highly similar to the current time are extracted from the road condition history database 111, and the possibility of traffic congestion occurring is high based on the location and time of occurrence of traffic congestion in the second condition. Estimate the location and time. In addition, it extracts from the road condition history database 111 the factors that trigger the occurrence of traffic congestion under the second condition. Vehicle behavior factors that trigger traffic congestion are, for example, a decrease in speed at a sag portion and sudden braking due to being interrupted. The congestion state estimation unit 123 may add, for example, gasoline prices, economic trends, etc. as conditions for estimating the congestion state. In this case, the traffic congestion estimation unit 123 acquires gasoline prices and economic index information for grasping economic trends from an external server device or the like via the reception unit 131 of the communication unit 130 . The congestion state estimation unit 123 outputs the prediction result to the trigger vehicle extraction unit 125 .

The vehicle travel information receiving unit 124 acquires travel information from each vehicle traveling around a point where congestion is expected to occur. The vehicle travel information reception unit 124 acquires travel information from each vehicle having a communication unit (not shown) via the reception unit 131 of the communication unit 130 . The travel information includes information such as the destination, speed/acceleration, accelerator/brake/steering operation, and duration of driving. The vehicle travel information reception unit 124 outputs the acquired travel information to the congestion situation estimation unit 123 and the trigger vehicle extraction unit 125 . The vehicle travel information receiving unit 124 may acquire an image of the surroundings of the vehicle or the vehicle occupant from an imaging unit (not shown) provided in each vehicle.

The vehicle travel information receiving unit 124 may acquire biological information including the driver's drowsiness, irritability, etc. as the travel information. The vehicle travel information receiving unit 124 may acquire information regarding the carbon dioxide (CO ₂ ) concentration in the vehicle, the audio volume, and the like as the travel information. In order to obtain such information, an image of the driver of the vehicle may be obtained to detect the line of sight of the driver. You may have a part. A sensor for measuring heart rate and pulse, and a sensor for measuring carbon dioxide concentration may be provided. That is, the vehicle travel information receiving unit 124 acquires various types of information that can affect the travel of the vehicle.

The trigger vehicle extraction unit 125 extracts trigger vehicles that are candidates for triggers that cause congestion. The trigger vehicle extracting unit 125 analyzes the relationship between the prediction information received from the traffic jam situation estimation unit 123 and the travel information received from the vehicle travel information receiving unit 124, and thus has a high possibility of triggering the occurrence of traffic congestion. Extract the trigger vehicle. The number of trigger vehicles to be extracted is not limited to one, and a plurality of vehicles may be extracted at the same time, or none may be extracted. For example, when there is a high possibility that the trigger for the occurrence of traffic congestion at the point estimated by the traffic congestion condition estimation unit 123 is frequent changes in the traveling speed, the trigger vehicle extraction unit 125 causes the vehicle traveling information reception unit 124 to acquire The amount of change in running speed is calculated from the running speed information of each vehicle, and vehicles that frequently change their running speed are extracted. In addition, the trigger vehicle extraction unit 125 extracts a vehicle with a large amount of brake operation from the accelerator/brake operation information of each vehicle acquired by the vehicle travel information reception unit 124 . Further, the trigger vehicle extraction unit 125 extracts, as the trigger vehicle, a vehicle having a remarkable speed change in the sag portion from the traveling speed information and position information of each vehicle acquired by the vehicle traveling information receiving unit 124 . It can be determined from the map information stored in the map database 112 whether or not the road on which the vehicle travels is a sag portion. For example, when there is a high possibility that the trigger for the occurrence of traffic congestion at a point estimated by the traffic congestion condition estimation unit 123 is the occurrence of a sudden braking operation due to a short inter-vehicle distance, the trigger vehicle extraction unit 125 extracts the vehicle travel information The inter-vehicle distance to the vehicle ahead is calculated from the brake operation information of each vehicle and the vehicle surroundings image information acquired by the receiving unit 124, and frequent braking operations caused by the short inter-vehicle distance are calculated based on the relationship between the inter-vehicle distance and the braking operation. to extract the vehicle. For example, when there is a high possibility that the trigger for the occurrence of traffic congestion at a point estimated by the traffic congestion condition estimation unit 123 is a vehicle behavior such as a lane change to bypass a vehicle parked on the road, the trigger vehicle extraction unit 125 Vehicles parked on the road are detected from the steering operation information and vehicle surrounding image information of each vehicle acquired by the vehicle travel information receiving unit 124, and the vehicles performing the steering operation to bypass the parked vehicles are extracted.

The trigger vehicle extraction unit 125, for example, on a highway, when the traffic congestion estimation unit 123 estimates that the trigger for the occurrence of traffic congestion at a certain point is the occurrence of a sudden brake operation due to the short distance between vehicles, the overtaking lane is selected. Vehicles traveling at low speed may be extracted as trigger vehicles. For example, the trigger vehicle extraction unit 125 detects vehicles traveling in areas where traffic restrictions are enforced when traffic restrictions such as inflow restrictions and speed restrictions are implemented due to accidents, bad weather, etc. on expressways. You may extract as a trigger vehicle.

The trigger vehicle extracting unit 125 estimates that, for example, on a general road, the traffic jam condition estimating unit 123 predicts that the trigger for the occurrence of traffic congestion at a certain point is an obstacle to smooth running due to vehicle behavior that detours a vehicle parked on the road. In this case, the vehicle parked on the road may be extracted as the trigger vehicle. The trigger vehicle extraction unit 125 may extract, as the trigger vehicle, a vehicle traveling in an area with traffic restrictions, even on a general road.

The proposing unit 126 proposes driving support information to the trigger vehicle extracted by the trigger vehicle extracting unit 125 so as not to perform a vehicle operation that could trigger traffic congestion. The proposal unit 126 makes a proposal by transmitting the driving support information as a notification signal to the trigger vehicle via the transmission unit 132 of the communication unit 130 . Specifically, for example, the proposal unit 126 confirms and maintains the vehicle speed of a trigger vehicle whose vehicle speed is likely to decrease immediately before traveling through a sag portion that is assumed to be a traffic congestion point. Suggest to do. For example, when it is estimated that an unreasonable interruption in the vicinity of a point where a traffic jam occurs or a lane change is a trigger for the occurrence of traffic congestion, the proposing unit 126 provides a trigger that is highly likely to cause these vehicle behaviors. It suggests to the vehicle to refrain from cutting in and changing lanes. For example, when the trigger of traffic congestion on an expressway is low-speed driving in the passing lane, the proposal unit 126 proposes to the trigger vehicle that is driving at low speed to return to the driving lane.

For example, when it is found that the behavior of a vehicle whose destination is relatively far away or has been continuously driven for a predetermined time or more has a strong correlation with the traffic volume, the proposal unit 126 proposes a break. You may

The proposal unit 126 may propose driving assistance information to vehicles traveling around the trigger vehicle so as to pay attention to the behavior of the trigger vehicle. Specifically, for example, when it is estimated that a vehicle running behind the trigger vehicle is slowing down, the proposal unit 126 is installed in a communication unit (not shown) provided in each vehicle or on the road. It is suggested that caution should be exercised in reducing the speed of the trigger vehicle by means such as displaying information on an information bulletin board. Alternatively, it may be suggested to keep a sufficient distance between the vehicle and the trigger vehicle. The proposal unit 126 may make a proposal to warn vehicles traveling around the trigger vehicle that the trigger vehicle may suddenly interrupt the vehicle. Specifically, for example, when it is estimated that a vehicle traveling in a lane adjacent to the trigger vehicle will cut in due to the sudden lane change of the trigger vehicle, the proposal unit 126 instructs the vehicle to pay attention to the lane change of the trigger vehicle. suggest. Alternatively, a proposal may be made to keep a sufficient inter-vehicle distance between the own vehicle and the preceding vehicle. In this manner, the proposal unit 126 proposes to the trigger vehicle not to perform a vehicle operation that may trigger traffic congestion, and warns vehicles traveling around the trigger vehicle about the behavior of the trigger vehicle. I suggest that In other words, the driving support information proposal for the trigger vehicle and the driving support information proposal for the vehicles traveling around the trigger vehicle are made in different forms, thereby making the traffic jam less likely to occur. can be

The proposal unit 126 may make the same proposal to a plurality of vehicles having similar driving characteristics among vehicles traveling around the trigger vehicle. A similar running characteristic means that the running speed and the amount of change in the running speed are similar. For example, the proposal unit 126 proposes that a plurality of vehicles having similar driving characteristics should travel in the same lane. Specifically, the proposal unit 126 makes an individual proposal to avoid the occurrence of congestion as a whole to a plurality of vehicles having similar driving performance among the vehicles traveling around the trigger vehicle.

For example, on a general road or the like, the proposal unit 126 slows down a vehicle running around a vehicle parked on the road or a vehicle waiting to turn right, and passes other vehicles running around the vehicle. It may also be possible to make a suggestion that the vehicle should run smoothly and request cooperation in driving to prevent a decrease in traffic volume.

Communication control unit 127 controls communication unit 130 . The communication control unit 127 performs information communication with an external information processing device by controlling the communication unit 130 . Communication control unit 127 controls receiving unit 131 to receive various information from an external information processing device. The communication control unit 127 controls the transmission unit 132 to transmit various information to an external information processing device.

The communication unit 130 has a receiving unit 131 and a transmitting unit 132 . The communication unit 130 is connected to the wired or wireless network 300 shown in FIG. 1, and performs information communication between the congestion prevention server 100 and an external information processing device under the control of the communication control unit 128 .

The communication unit 130 performs information communication between the congestion prevention server 100 and the vehicle-mounted device 200 under the control of the communication control unit 128, for example. In this case, the receiving unit 131 receives the traveling information of the vehicle from the vehicle-mounted device 200 . The transmission unit 132 transmits the proposal of the proposal unit 126 to the vehicle-mounted device 200 as a notification signal.

The communication unit 130 performs information communication between the congestion prevention server 100 and an external server device, for example, under the control of the communication control unit 128 . In this case, the receiving unit 131 receives various conditions regarding road conditions.

The configuration of the vehicle-mounted device 200 will be described with reference to FIG.

As shown in FIG. 3 , the vehicle-mounted device 200 includes a storage section 210 , a control section 220 , a communication section 230 and an audio output section 240 .

The storage unit 210, for example, the control unit 220, stores a program for realizing the function of each unit. In this case, the control unit 220 expands and executes the programs stored in the storage unit 210, thereby realizing the functions of the respective units. The storage unit 210 can be realized by, for example, semiconductor memory devices such as RAM, ROM, and flash memory, or storage devices such as hard disks, solid state drives, and optical disks.

The control unit 220 includes an own vehicle travel information acquisition unit 221 , a communication control unit 222 and an output control unit 223 . The control unit 220 expands and executes the programs stored in the storage unit 210 to realize the functions of each unit. The control unit 220 can be realized by an electronic circuit including a CPU, for example.

The host vehicle travel information acquisition unit 221 acquires travel information of the host vehicle on which the vehicle-mounted device 200 is mounted. The host vehicle travel information acquisition unit 221 acquires travel information from a CAN (Controller Area Network) or various sensors provided in the host vehicle.

The communication control section 222 controls the communication section 230 . The communication control unit 222 performs information communication with an external information processing device by controlling the communication unit 230 . The communication control unit 222 controls the receiving unit 231 to receive various information from an external information processing device. The communication control unit 222 receives the notification signal from the congestion prevention server 100 by controlling the reception unit 231, for example. The communication control unit 222 controls the transmission unit 232 to transmit various information to an external information processing device. The communication control unit 222 , for example, controls the transmission unit 232 to transmit the travel information of the own vehicle acquired by the own vehicle travel information acquisition unit 221 to the congestion prevention server 100 .

The output control section 223 controls the audio output section 240 . The output control unit 223 controls the audio output unit 240 to output audio. The output control unit 223 outputs, for example, driving assistance information corresponding to the notification signal received from the congestion prevention server 100 from the audio output unit 240 as audio.

The communication unit 230 has a receiving unit 231 and a transmitting unit 232 . The communication unit 230 is connected to the wired or wireless network 300 shown in FIG. 1, and performs information communication between the vehicle-mounted device 200 and an external information processing device under the control of the communication control unit 222 .

The communication unit 230 performs information communication between the congestion prevention server 100 and the vehicle-mounted device 200 under the control of the communication control unit 222, for example. In this case, the receiving unit 231 receives the notification signal from the congestion prevention server 100 . The transmission unit 232 transmits the travel information of the host vehicle to the congestion prevention server 100 .

The voice output unit 240 outputs various voices including suggestions from the congestion prevention server 100 . The audio output unit 240 can be implemented by, for example, a speaker for an audio device installed in the vehicle.

Processing of the control unit 120 of the congestion prevention server 100 will be described with reference to FIG. FIG. 5 is a flowchart showing an example of the processing flow of the control unit 120 of the congestion prevention server 100. As shown in FIG.

First, the control unit 120 acquires the current road conditions (step S101). Specifically, the control unit 120 acquires conditions such as road traffic volume, weather, and temperature at the current date and time from an external server device, for example. Then, the process proceeds to step S102.

The control unit 120 searches the road condition history database 111 for road conditions similar to the conditions acquired in step S101 (step S102). Specifically, the control unit 120 performs a search based on similarity, for example, that the present time is the same time as the previous week. Then, the process proceeds to step S103.

Based on the search result in step S102, the control unit 120 estimates the congestion occurrence point and the cause of the congestion under the conditions acquired in step S101 (step S103). Then, the process proceeds to step S104.

The control unit 120 acquires the travel information of each vehicle traveling around the traffic congestion point (step S104). Then, the process proceeds to step S105.

The control unit 120 extracts a trigger vehicle, which is a candidate vehicle to cause congestion, based on the cause of the congestion estimated in step S103 and the travel information acquired in step S104 (step S105). In step S105, the control unit 120 may extract vehicles traveling around the trigger vehicle. Then, the process proceeds to step S106.

The control unit 120 transmits, as a notification signal, driving assistance information for preventing traffic congestion to the trigger vehicle (step S106). In step S106, the control unit 120 may transmit, as a notification signal, driving assistance information to the vehicles traveling around the trigger vehicle, indicating that attention should be paid to the behavior of the trigger vehicle. Then, the processing of FIG. 5 ends.

As described above, in this embodiment, it is possible to search for past road conditions that are similar to current road conditions, and to estimate the current congestion point and cause of congestion. As a result, the present embodiment acquires the travel information of each vehicle traveling around the point where the congestion occurs, extracts the trigger vehicle that causes the congestion, and proposes to suppress the occurrence of the congestion with respect to the trigger vehicle. can do As a result, this embodiment can appropriately prevent the occurrence of traffic congestion.

In addition, in the present embodiment, in order to avoid the occurrence of congestion on the entire road where congestion is expected to occur, vehicle behavior that is assumed to be a cause of congestion for each location where congestion is expected to occur Appropriate proposals can be made for each vehicle that is traveling with many behaviors that can cause congestion. As a result, this embodiment can prevent traffic congestion on the road as a whole.

[Second embodiment]
An example of the configuration of the vehicle-mounted device according to the second embodiment will be described with reference to FIG. FIG. 6 is a block diagram showing an example of the configuration of the vehicle-mounted device according to the second embodiment.

As shown in FIG. 6, the vehicle-mounted device 200A includes a storage section 210A, a control section 220A (congestion prevention device), a communication section 230, and an audio output section 240. As shown in FIG. 200 A of vehicle-mounted devices have the storage part 210A memorize|stored the learning data 211, and 220 A of control parts are equipped with the condition acquisition part 224, the traffic jam condition estimation part 225, the determination part 226, and the proposal part 227. This is different from the vehicle-mounted device 200 according to the first embodiment. In the first embodiment, the server side presumes the traffic congestion situation, but in the second embodiment, the vehicle-mounted device side presumes the traffic congestion situation.

The learning data 211 is learning data in which correlations between various conditions and road traffic volumes are modeled by machine learning such as deep learning using a neural network. Various conditions include, for example, information such as date and time, weather, temperature, and events being held in the surrounding area. The learning data 211 is, for example, learning data estimated by the traffic jam situation estimation unit 123 of the traffic congestion prevention server 100 .

The condition acquisition unit 224 acquires various conditions regarding road conditions. The condition acquisition unit 224 acquires various conditions regarding current road conditions at a predetermined point from an external server device or the like via the reception unit 231 of the communication unit 230 . The condition acquisition unit 224 may, for example, acquire various information about road conditions from a roadside device such as an ITS placed on the road. That is, the condition acquisition unit 224 has the same function as the condition acquisition unit 121 shown in FIG. Therefore, the various conditions acquired by the condition acquisition unit 224 are the same as the conditions acquired by the condition acquisition unit 121 .

The congestion state estimation unit 225 estimates a possible traffic congestion state based on the learning data 211 and the conditions acquired by the condition acquisition unit 224 . Based on the learning data 211 , the traffic congestion state estimation unit 225 estimates the current traffic congestion point and the vehicle behavior that triggers the occurrence of traffic congestion at the traffic congestion point. In other words, the congestion state estimation unit 225 estimates the congestion occurrence point and the vehicle behavior that triggers the occurrence of congestion at the congestion occurrence point using a machine learning technique.

The determining unit 226 determines whether or not the own vehicle is the trigger vehicle based on the travel information of the own vehicle and the estimation result of the traffic jam condition estimation unit 225 .

When the determining unit 226 determines that the own vehicle is the trigger vehicle, the proposing unit 227 makes a proposal to the driver of the own vehicle to prevent the occurrence of traffic congestion. In this case, the output control unit 223 makes a proposal by voice from the voice output unit 240 under the control of the proposal unit 227 .

Processing of the control unit 220A of the vehicle-mounted device 200A according to the second embodiment will be described with reference to FIG. FIG. 7 is a flow chart showing an example of the flow of processing of the control unit 220A of the vehicle-mounted device 200A according to the second embodiment.

First, the control unit 220A acquires the travel information of the own vehicle via CAN or the like (step S201). Then, the process proceeds to step S202.

220 A of control parts acquire the various conditions regarding a road condition at present (step S202). Then, the process proceeds to step S203.

Next, the control unit 220A estimates the traffic jam point and traffic jam cause on the current road based on the conditions acquired in step S201 and the learning data 211 (step S203). Then, the process proceeds to step S204.

Next, the control unit 220A determines whether or not the own vehicle is the trigger vehicle based on the travel information of the own vehicle acquired in step S201 and the estimation result estimated in step S203 (step S204).

When it is determined that the own vehicle is not the trigger vehicle (No in step S204), the control unit 220A proceeds to step S201. On the other hand, if the own vehicle is determined to be the trigger vehicle (Yes in step S204), the process proceeds to step S205.

At step S205, the control unit 220A makes a proposal to the driver of the host vehicle to prevent congestion (step S205). Then, the processing of FIG. 7 ends.

As described above, in the present embodiment, the vehicle side can estimate the congestion occurrence point and the congestion occurrence factor at the present time based on learning data obtained by learning the correlation between the road condition and the traffic volume. Then, according to the present embodiment, when it is determined that the own vehicle is the trigger vehicle based on the estimation result, it is possible to make a proposal to the driver to prevent the occurrence of traffic congestion. As a result, the present embodiment can predict the occurrence of traffic congestion and prevent the occurrence of traffic congestion without constant communication between the server device and the vehicle-mounted device.

Although the embodiments of the present invention have been described above, the embodiments are not limited by the contents of these embodiments. In addition, the components described above include those that can be easily assumed by those skilled in the art, those that are substantially the same, and those within the so-called equivalent range. Furthermore, the components described above can be combined as appropriate. Furthermore, various omissions, replacements, or modifications of components can be made without departing from the gist of the above-described embodiments.

1 traffic jam prevention system 100 traffic jam prevention server 110, 210, 210A storage unit 111 road condition history database 112 map database 120, 220 control unit 121, 224 condition acquisition unit 122 road condition search unit 123, 225 traffic condition estimation unit 124 vehicle travel information Receiver 125 Trigger vehicle extractor 126 Proposal unit 127, 222 Communication controller 130 Communication unit 131 Receiver 132 Transmitter 200, 200A Vehicle-mounted device 211 Learning data 220A Control unit (congestion prevention device)
221 self-vehicle travel information acquisition unit 223 output control unit 240 audio output unit

Claims (8)

a road condition search unit that searches a road condition history database for road conditions under a second condition that has date and time similarity with respect to the designated first condition;
a congestion situation estimation unit for estimating a congestion occurrence point and a congestion occurrence factor under the first condition based on the road situation under the second condition;
a vehicle travel information receiving unit that receives travel information of each vehicle traveling under the first condition;
a trigger vehicle extracting unit that extracts a trigger vehicle that may serve as a trigger for causing congestion from the traffic congestion cause and the travel information;
a transmission unit that transmits driving support information as a notification signal to the trigger vehicle;
Traffic jam prevention server. Road condition search for searching the road condition history database for a road condition under a second condition that has similarity to a specified first condition, where the traffic volume is increased or decreased within a predetermined value compared to the first condition. Department and
a congestion situation estimation unit for estimating a congestion occurrence point and a congestion occurrence factor under the first condition based on the road situation under the second condition;
a vehicle travel information receiving unit that receives travel information of each vehicle traveling under the first condition;
a trigger vehicle extracting unit that extracts a trigger vehicle that may serve as a trigger for causing congestion from the traffic congestion cause and the travel information;
a transmission unit that transmits driving support information as a notification signal to the trigger vehicle;
Traffic jam prevention server. the travel information includes at least one of the position, speed, acceleration, and amount of braking of the vehicle;
The trigger vehicle extraction unit extracts a trigger vehicle that may trigger the congestion based on changes in the travel information of the vehicle.
The congestion prevention server according to claim 1 or 2 . The transmission unit transmits the driving support information as the notification signal to vehicles around the trigger vehicle in the vicinity of the traffic congestion point.
The congestion prevention server according to any one of claims 1 to 3 . a road condition search unit that searches a road condition history database for each road condition in a second condition that has similarity in terms of date and time with respect to the designated first condition;
a congestion situation estimation unit that estimates a congestion occurrence point and a congestion occurrence factor under the first condition based on a plurality of road conditions under the second condition;
an own vehicle travel information acquisition unit that acquires travel information of a vehicle that travels under the first condition;
a trigger vehicle extracting unit that extracts a trigger vehicle that may serve as a trigger for causing congestion from the traffic congestion cause and the travel information;
a transmission unit that transmits driving support information as a notification signal to the trigger vehicle;
A traffic jam prevention device. A traffic jam prevention server according to any one of claims 1 to 4;
A traffic jam prevention system comprising a vehicle device,
The vehicle device includes:
a vehicle travel information transmission unit that acquires vehicle travel information and transmits it to the congestion prevention server;
a receiving unit that receives the driving support information from the congestion prevention server as a notification signal;
comprising a
Congestion prevention system. the computer
a road condition search step of searching a road condition history database for road conditions under a second condition having date and time similarity with respect to the specified first condition;
a congestion situation estimation step of estimating a congestion occurrence point and a congestion occurrence factor under the first condition based on the road condition under the second condition;
a vehicle travel information receiving step of receiving travel information of each vehicle traveling under the first condition;
a trigger vehicle extracting step of extracting a trigger vehicle that may serve as a trigger for causing congestion from the traffic congestion cause and the travel information;
a transmitting step of transmitting driving support information as a notification signal to the trigger vehicle;
traffic congestion prevention method. a road condition search step of searching a road condition history database for road conditions under a second condition having date and time similarity with respect to the designated first condition;
a congestion situation estimation step of estimating a congestion occurrence point and a congestion occurrence factor under the first condition based on the road conditions under the second condition;
a vehicle travel information receiving step of receiving travel information of each vehicle traveling under the first condition;
a trigger vehicle extracting step of extracting a trigger vehicle that may serve as a trigger for causing congestion from the traffic congestion cause and the travel information;
a transmitting step of transmitting driving support information as a notification signal to the trigger vehicle;
is executed by a computer operating as a traffic jam prevention server.

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