JP7429118B2 - Traffic jam prediction method and traffic jam prediction device - Google Patents

Description

The present invention provides a method and method for predicting traffic jams that allows anyone to easily measure traffic congestion information, collect this information, and predict traffic jams from the collected data so that users can easily use the predictive data. Regarding equipment.

2. Description of the Related Art A method has been proposed in which the amount of vehicles passing on a highway is detected by their speed, and traffic congestion is predicted based on this information (Patent Document 1). However, this method is not real-time, requires fixed points, and requires large-scale equipment.

Therefore, a method of predicting traffic congestion based on information acquired by the own vehicle has also been proposed (Patent Document 2). According to this known method, while detecting the speed of the own vehicle, an image of the passing lane in front of the own vehicle is captured, and when the passing of two vehicles on the passing lane is detected, the The system calculates the time difference, inter-vehicle distance, and speed, determines the traffic volume in the overtaking lane, and uses this traffic volume to predict congestion.

However, the above-mentioned conventional method has a problem in that it requires a large computational load such as calculating traffic volume, and it is not possible to predict traffic congestion until two vehicles in front have passed, making real-time measurement impossible. Furthermore, when two vehicles in front pass, there is a drawback that if the preceding vehicle is in the shadow of the vehicle behind at the time of photographing, measurement may not be possible or the error will be large even if it is measured.

Japanese Patent Application Publication No. 2006-309735 Japanese Patent Application Publication No. 2019-16081

The present invention focuses on the above-mentioned conventional problems, and quickly ascertains the inter-vehicle distance between two vehicles in the front and rear on adjacent lanes, including the overtaking lane, with little error during measurement, and uses the collected inter-vehicle distance information. It is an object of the present invention to provide a traffic congestion prediction method and a traffic congestion prediction device that can more accurately predict traffic congestion and provide convenience to users of traffic congestion prediction data using inter-vehicle distance information. purpose.

The present invention enables anyone who has a carry-on device such as a smartphone to easily obtain information on whether or not there will be a traffic jam at a low cost, and by collecting this data, it is possible to predict traffic jams. The goal is to make it possible to provide convenience to users.

In order to achieve the above object, the traffic jam prediction method according to the present invention, when predicting traffic congestion, photographs the front and rear sides using a photographing means installed in the own vehicle, and The distance between the vehicle and the vehicle behind it is used.

Furthermore, the present invention calculates the inter-vehicle distance between a forward vehicle traveling in a lane adjacent to the host vehicle's driving lane and a rear vehicle following it from captured images in front and behind the vehicle, and outputs this inter-vehicle distance. , the output inter-vehicle distance is collected, and traffic congestion is predicted based on the collected inter-vehicle distance information.

The inter-vehicle distance can be calculated by photographing the front and rear sides using a photographing means to obtain a front inter-vehicle distance and a rear inter-vehicle distance, and adding the distances between the photographing means. It is possible to separately acquire an image of the side of the own vehicle.

The present invention calculates the distance between front and rear vehicles in a driving lane adjacent to the driving lane of the own vehicle using a photographing means capable of photographing all directions of the vehicle, outputs this distance between cars, and collects the outputted distance between cars, It is possible to predict traffic jams based on the collected inter-vehicle distance information.

The output of the inter-vehicle distance may be temporarily stopped to prevent erroneous detection of the inter-vehicle distance when a vehicle traveling in an adjacent driving lane becomes a blind spot in the photographed images in front of and behind the host vehicle.

When a vehicle traveling in an adjacent driving lane becomes a blind spot in the photographed image in front of or behind the own vehicle, it is assumed that the vehicle in the blind spot is right next to the own vehicle, and the vehicle in front or behind in the photographed image is It is also possible to adopt a configuration in which the inter-vehicle distance between one of the vehicles closer to the own vehicle and the own vehicle is the inter-vehicle distance between adjacent vehicles.

The traffic congestion prediction device according to the present invention includes a photographing means installed in a host vehicle and photographing a front vehicle and a following rear vehicle traveling in a driving lane adjacent to the own vehicle travel lane, and a traffic jam prediction device based on data from the photographing means. The configuration includes calculation means for determining the distance between the front and rear vehicles. Furthermore, the present invention provides a photographing means installed in the own vehicle for photographing a front vehicle traveling in a driving lane adjacent to the own vehicle driving lane and a following rear vehicle, and a distance between the front and rear vehicles based on data from the photographing means. A configuration of a traffic jam prediction device comprising: a calculation means for calculating a distance; and a means for collecting the inter-vehicle distance and measured position information output from the calculation means and predicting congestion based on the inter-vehicle distance information for each measured position. did.

The present invention also provides photographing means installed at the front and rear of the own vehicle and facing forward and rearward, and based on the images taken by the photographing means, the distance between the front vehicles of a vehicle traveling in a lane adjacent to the own vehicle lane. A calculation means for calculating the distance between the following rear vehicles, a clock means for determining the measurement time by this calculation means, a position information acquisition means mounted on the own vehicle, and the above calculation means, clock means, and position information acquisition means can be output. It is also possible to adopt a configuration including a communication means and a server that receives data from the communication means and outputs the collected traffic congestion information.

A configuration may also be adopted in which a mobile terminal having the photographing means, calculation means, clock means, and position information acquisition means is used. Furthermore, a configuration may be adopted in which a photographing means capable of photographing the side of the own vehicle is added. Further, the photographing means may be a photographing device having a viewing angle of 360 degrees.

It is also possible to collect measured position information output from a position information acquisition device such as a GPS and inter-vehicle distance output from a calculation means, and predict traffic jams based on the inter-vehicle distance information for each measured position.

The present invention quickly ascertains the inter-vehicle distance between two vehicles in the adjacent lane with little error during measurement, and uses this information as the collected inter-vehicle distance information to predict traffic jams, making it possible to predict traffic congestion more accurately. This makes it possible to provide convenience to users.

FIG. 1 is an overall block configuration diagram illustrating a traffic congestion prediction device according to an embodiment. FIG. 1 is a configuration diagram of a traffic congestion prediction device according to an embodiment. It is a flowchart which shows the whole algorithm of the front portable terminal of the traffic jam prediction method based on an Example. It is a flowchart which shows the measurement algorithm of the front portable terminal of the same method. It is a flowchart which shows the communication algorithm of the front portable terminal of the same method. It is a flowchart which shows the whole algorithm of the rear portable terminal of the same method. It is a flowchart showing the measurement algorithm of the rear mobile terminal of the same method. It is a flowchart which shows the communication algorithm of the rear portable terminal of the same method. FIG. 3 is a top view of a traveling vehicle showing a second embodiment of the present invention. FIG. 2 is a plan perspective view of a vehicle showing a second embodiment. FIG. 7 is a plan perspective view of a vehicle showing a third embodiment. It is a top view of the front and rear vehicles of 3rd Example.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of a traffic congestion prediction method and a traffic congestion prediction device of the present invention will be described in detail with reference to the drawings. It should be noted that the embodiments shown below are some of the preferred modes for carrying out the present invention, and as long as the same effects are achieved, the present invention may be implemented even if some of the configurations and methods are changed. It can be considered as part of

FIG. 1 is a block diagram of a traffic congestion prediction method according to an embodiment, and FIG. 2 is a block diagram of a traffic congestion prediction device.

As shown in these figures, in this embodiment, the front vehicle 16 traveling in the adjacent lane 14 and the following rear vehicle 18 as seen from the host vehicle 12 traveling in the driving lane 10 are displayed as a video using a smartphone. I'm capturing it. As shown in FIG. 1, there is a front smartphone 20 that is mounted on the front of the host vehicle 12 and is capable of photographing the front, and a rear smartphone 22 that is similarly mounted on the rear and capable of photographing the rear. Thus, a front image 24 and a rear image 26 are respectively photographed.

As shown in FIG. 1, the inter-vehicle distance h _L between the running front vehicle 16 and the following rear vehicle 18 is equal to the distance h ₂ from the position of the photographing device 21 of the front smartphone 20 to the front vehicle 16. , is the added value of the distance h ₃ from the position of the photographing device 23 of the rear smartphone 22 to the rear vehicle 18 and the distance h _ii between the smartphones 20 and 22. That is, the inter-vehicle distance h _L = h ₂ + h ₃ + h _ii . In order to obtain this, in the embodiment, the distance between the front vehicles is measured using image processing. Further, the distance h _ii between the smartphones 20 and 22 may be measured in advance. Therefore, the inter-vehicle distance h _L can be calculated only from the image.

In order to obtain the inter-vehicle distance h _L =h ₂ +h ₃ +h _ii , it is necessary to collect data from the front (rear) smartphone 20 and the rear (front) smartphone 22 . For this purpose, as shown in FIG. 2, the short-range communication means 30 of the smartphones 20 and 22 is used. That is, the front (rear) smartphone 20 performs the addition process. This addition is performed by a processing device 32 serving as an arithmetic unit, stored in a built-in storage device 33, and sent to, for example, a server 28 by a long-distance communication device 34, which will be described later.

At the time of addition, the short-range communication means 30 sends the inter-vehicle distance _h3 measured by the rear smartphone 22 and the measurement time obtained by the clock device 38 to the front smartphone 20, and the inter-vehicle distance h2 measured by the front smartphone 20 and the inter-vehicle distance _h2 measured by the front smartphone 20 are sent. , the distance h _ii between the photographing devices 21 and 23 that has been measured in advance and held in the storage device 33 is read out, and the processing device 32 calculates the inter-vehicle distance h _L and stores it in the storage device 33 .

Next, this inter-vehicle distance h _L is output, and the output inter-vehicle distance h _L is collected from a plurality of vehicles 12 _n . The collection destination is, for example, the server 28. In order to collect information, the smartphones 20 and 22 are equipped with a long-distance communication device 34 as described above, but this also uses the position information acquisition device 36 installed in the smartphones 20 and 22 to measure the inter-vehicle distance. I am trying to send time location information. The location information acquisition device uses, for example, a GPS installed in a smartphone. Further, since the inter-vehicle distance h _L is the sum of the forward inter-vehicle distance h ₂ and the rear inter-vehicle distance h ₃ , the times at which the additions are made must match. Therefore, a clock device 38 is provided to obtain the measurement times of the forward inter-vehicle distance h ₂ and the rear inter-vehicle distance h ₃ . If the time difference measured by the clock device 38 is greater than a certain time, the added data is not used. The thus processed data is sent to the server 28 by the long distance communication device 34.

Then, as described above, the server 28 predicts traffic congestion based on the collected inter-vehicle distance information {h _L }. For example, if the inter-vehicle distance is less than 40 meters for a certain period of time, it will provide information that traffic jams will occur in the future. The server 28 that determines this feeds the traffic congestion information into the transportation network it has created, quickly creates traffic congestion information from the collected inter-vehicle distance information {h _L }, and provides information to transport companies, bus companies, taxi companies, expressway companies, etc. The user 40 pays a usage fee 42 and can utilize the traffic congestion information 44.

The specific processing procedure will be explained below with reference to FIGS. 3 to 8.
FIG. 3 shows the overall processing of the front smartphone 20. Initially, the front smartphone measurement algorithm starts (S100). In this measurement algorithm, as shown in FIG. 4, an image is captured using the imaging device 21 (S101). Thereafter, the presence or absence of the preceding vehicle 16 is confirmed (S102), and it is determined whether the recognized vehicle is in the adjacent lane 14 (S103). If it does not appear, the process ends, and if it does, the distance is calculated using image processing (S104). As shown in FIG. 3, when the measurement is completed, the information is stored in the storage device 33 as the distance between the front vehicles (S110). If there is no information on the distance h ₂ between the front vehicles, the process is terminated as there is no information on the traffic jam at that point (S170), and if there is information, the process proceeds to the next front smartphone communication algorithm (S120).

As shown in FIG. 5, it is checked whether or not it is possible to communicate with the other party's smartphone 22 from the short-range communication means 30 as a communication means (S121). If it is not possible, the work is finished, and if possible, the measured inter-vehicle distance h ₂ and the measurement time of the other party's smartphone 22 is received (S122). Then, it is checked whether the time difference when measuring the inter-vehicle distance h ₂ and h ₃ of the front and rear smartphones 20 and 22 is within a certain time (S123), and if it is within a certain time, it is stored in the storage device 33, and if it is not, it is stored in the storage device 33. Reset and exit. This is to allow the driver to judge whether or not the measured inter-vehicle distances h ₂ and h ₃ are correct. When this is completed, the process returns to the overall algorithm and calculates the distances h ₂ and h ₃ between the front and rear vehicles (S130). The distance h _ii between the photographing devices 21 and 23 is added to obtain the inter-vehicle distance h _L , and it is determined whether this is greater than or less than the specified value of 40 m (S140). This is based on research results that assume that the inter-vehicle distance h _L is 40 m, and that if it is less than 40 m, traffic jams will begin in the future (Shincho Sensho, Congestion Studies, written by Katsuhiro Nishinari, p. 48~). Of course, you may change this value. In order to exclude special situations such as when the inter-vehicle distance h _L momentarily shortens regardless of the traffic condition, we check whether the period in which the measured inter-vehicle distance h _L is less than the specified value continues for 3 minutes. A judgment is made (S150). Of course, this time can be changed. If the inter-vehicle distance h _L continues to be less than the specified value for a certain period of time or more, the point is stored in the storage device 33 as a sign of traffic congestion or as being in traffic jam (S160).

As can be seen from FIG. 6, the overall algorithm for the rear smartphone 22 first implements a measurement algorithm for the rear smartphone 22 (S200). This algorithm works the same way as before. That is, as shown in FIG. 7, an image is photographed using the photographing device 23 (S201). Thereafter, the presence or absence of the vehicle 18 behind is confirmed (S202), and it is determined whether the recognized vehicle is in the adjacent lane 14 (S203). If it does not appear, the process ends, and if it does, the distance is calculated using image processing (S204). When the measurement is completed, the information is stored in the storage device 33 as the rear inter-vehicle distance (S210). Returning to the overall algorithm on the rear smartphone 22 side in Figure 6, if there is no information on the rear inter-vehicle distance _h3 , the work is terminated with no traffic jam information at that point, and if there is information, the process proceeds to the next rear smartphone communication algorithm. (S220). It is checked whether or not it is possible to communicate with the other party's front smartphone 20 from the short-range communication means 30 as a communication means (S221), and if it is not possible, the work is finished, and if possible, the measured rear inter-vehicle distance h ₃ and the time measured by the clock device 38 (S222), and the process ends.

In this way, the time difference when measuring the front and rear vehicle distances h ₂ and h ₃ of the front and rear smartphones 20 and 22 is checked, and the front and rear vehicle distances h ₂ and h ₃ are calculated. The distance h _ii between the photographing devices 21 and 23 is added to obtain the inter-vehicle distance h _L , and it is determined whether this distance is greater than or equal to a specified value or less than a specified value. If the measured inter-vehicle distance h _L is less than a specified value, that point is stored in the storage device 33 as a sign of traffic jam or as being in traffic jam.

This result is sent to the server 28 through the long-distance communication device 34, for example, and the server 28 collects the data, which can be used to predict traffic jams based on the collected inter-vehicle distance information {h _L }. For example, if a certain number of pieces of information have been collected indicating that the distance between vehicles is 40 meters or less, it will provide information that traffic jams will occur in the future. The server 28 that determines this feeds the traffic congestion information into the transportation network it has created, quickly creates traffic congestion information from the collected inter-vehicle distance information {h _L }, and provides information to transport companies, bus companies, taxi companies, expressway companies, etc. The user 40 pays the usage fee 42 and can utilize the traffic congestion information 44.

As described above, according to this embodiment, an image is obtained by capturing the front and rear views using an on-vehicle photographing device, the distance between the front vehicles and the distance between the rear vehicles is determined from the image, and the distance between the photographing devices is also added to determine the target adjacent vehicle. The distance between two vehicles on a line can be calculated. By linking this with location information and time information and sending it to a server via communication means, and collecting the sent data, it is possible to reflect in traffic information that traffic jams will occur in the future. Users can expect significant improvements if they utilize this data. At this time, anyone who has a mobile terminal such as a smartphone equipped with a photographing means, a position information acquisition means, a clock means, and a communication means can easily measure the vehicles in front and behind on the adjacent lane at a low cost. It can provide predictive information for traffic jams.

In the above embodiment, the distance between vehicles was measured using the smartphones 20 and 22, but in addition to attaching the photographing devices 21 and 23 at the front and rear, as shown in FIG. May be installed. The side smartphone 46 is a device having the same functions as the rear smartphone 22. Of course, a device with equivalent functionality can be replaced with the side smartphone 46. In this way, as shown in FIG. 10, when the vehicles are lined up in the adjacent lane 14, the speed of the front vehicle 16 and the following rear vehicle 18 increases, and when the vehicles are running parallel to the own vehicle 12, the speed of the vehicle behind the vehicle 18 increases. The vehicle 18 enters the blind spot of the front and rear photographing devices 21 and 23. At this time, since the side smartphone 46 is present, it is possible to photograph the rear vehicle 18, so that the correct inter-vehicle distance h _L can be measured. When measured by this side smartphone 46, the output of the inter-vehicle distance is temporarily stopped if a vehicle traveling in the adjacent lane 14 becomes a blind spot in the photographed images in front of and behind the own vehicle. , to prevent false detection of the following distance. In addition, when a vehicle traveling in an adjacent driving lane becomes a blind spot in the photographed images before and after the own vehicle, it is assumed that the vehicle 18 in the blind spot is right next to the own vehicle 12, and the front side in the photographed image is Alternatively, the inter-vehicle distance between one of the rear vehicles closer to the own vehicle 12 and the own vehicle may be set as the inter-vehicle distance between the adjacent vehicles.

Furthermore, as shown in FIG. 11, a 360-degree camera 48 is installed inside or outside the host vehicle 12 to capture a front image 24 and a rear image 26, and performs image processing using a calculation device. The inter-vehicle distance between the front vehicle 16 and the rear vehicle 18 may be determined from the size and position. According to this, as shown in FIG. 12, when a vehicle in an adjacent lane is to the side of the own vehicle 12, the front and rear photographing devices 21 and 23 are in the blind spot of the photographing device, but the 360-degree camera 48 can take images in all directions. This makes it possible to measure the inter-vehicle distance h _t more accurately, and it is also possible to take pictures by simply installing one photographing device, which has the effect of reducing costs.

In the embodiment, a smartphone with a photographing function, a position information acquisition function, a clock function, and a communication function is used, but the present invention can be realized by means having independent functions.

The present invention can predict traffic congestion based on the distance between vehicles, and anyone can easily use it and provide it to server users.

10... Own vehicle driving lane, 12... Own vehicle, 14... Adjacent lane (adjacent driving lane), 16... Front vehicle, 18... Rear vehicle, 20... Front smartphone, 22... Rear smartphone, 24...Front image, 26...Back image, 28...Server, 30...Short range communication means, 32...Processing device, 33...Storage device, 34...Long distance communication device, 36...Position information Acquisition device, 38... Clock device, 40... User, 42... Usage fee, 44... Traffic jam information, 46... Side smartphone, 48... 360 degree camera.

Claims (12)

When predicting traffic jams, images are taken of the front and rear sides using a camera mounted on the vehicle, and the distance between the vehicle in front and the vehicle following the vehicle in the lane adjacent to the lane in which the vehicle is traveling is captured. , the time and position information at which the inter-vehicle distance was obtained are acquired, and if the inter-vehicle distance is less than a set value and its duration exceeds the set value, it is determined that there is a sign of traffic congestion or that the vehicle is in a traffic jam. A traffic congestion prediction method characterized by storing data in a storage device . Claim: 1. The inter-vehicle distance can be calculated by photographing the front and rear sides using photographing means to determine the forward and rear inter-vehicle distances, and adding the distances between the photographing means. The traffic congestion prediction method described in 1. 2. The traffic jam prediction method according to claim 1, wherein an image of the side of the own vehicle can be separately acquired. The distance between the front and rear vehicles in the driving lane adjacent to the vehicle driving lane is calculated using a photographing means that can take pictures of the vehicle in all directions. 2. The method for predicting traffic jams according to claim 1, further comprising predicting traffic jams based on distance information. A claim characterized in that when a vehicle traveling in an adjacent driving lane becomes a blind spot in the photographed images in front of and behind the own vehicle, the output of the inter-vehicle distance is temporarily stopped to prevent erroneous detection of the inter-vehicle distance. The traffic congestion prediction method described in 1. When a vehicle traveling in an adjacent driving lane becomes a blind spot in the photographed image in front of or behind the own vehicle, it is assumed that the vehicle in the blind spot is right next to the own vehicle, and the vehicle in front or behind in the photographed image is 2. The congestion prediction method according to claim 1, wherein the inter-vehicle distance between one of the vehicles closer to one's own vehicle and one's own vehicle is set as the inter-vehicle distance between adjacent vehicles. a photographing means installed in the own vehicle for photographing a front vehicle traveling in a driving lane adjacent to the own vehicle driving lane and a following rear vehicle;
a calculating means for calculating a distance between the front and rear vehicles based on data from the photographing means;
Clock means for determining the time measured by the calculation means;
A location information acquisition means installed in the own vehicle,
a storage device that determines that there is a sign of a traffic jam or a traffic jam is occurring when the inter-vehicle distance is less than a set value and the measured time exceeds a set value;
A traffic jam prediction device comprising : Photographic means installed at the front and rear of the own vehicle and directed toward the front and rear;
Calculating means for calculating the distance between the front vehicle and the distance between the following vehicles based on the image taken by the photographing means, of a vehicle traveling in a lane adjacent to the vehicle lane;
Clock means for determining the measurement time by the calculation means;
A location information acquisition means installed in the own vehicle,
a storage device that determines that there is a sign of a traffic jam or a traffic jam is occurring when the inter-vehicle distance is less than a set value and the measured time exceeds a set value based on the calculation means, clock means, and position information acquisition means;
a communication means capable of outputting data in the storage device ;
A traffic jam prediction device comprising: 9. The traffic congestion prediction device according to claim 7, wherein a mobile terminal having the photographing means, the calculating means, the clock means, and the position information acquiring means is used. The traffic congestion prediction device according to claim 7 or 8, further comprising a photographing means capable of photographing the side of the own vehicle. 9. The traffic jam prediction device according to claim 7, wherein the photographing means is a photographing device having a viewing angle of 360 degrees. 9. Traffic jam prediction according to claim 7 or 8 , wherein the measured position information output from the position information acquisition device and the inter-vehicle distance output from the calculation means are collected, and traffic jam information is predicted based on the inter-vehicle distance information for each measured position. Device.

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