JP7131990B2 - Traffic condition determination system and traffic condition determination device - Google Patents

Description

The present invention relates to a traffic condition determination system and a traffic condition determination device for determining congestion conditions.

2. Description of the Related Art Conventionally, traffic information acquired from an external center or the like has been provided as one of the driving support when a user moves on a road. Traffic information includes, for example, information on traffic jams, road closures, accidents, lane restrictions, and the like occurring on roads. Here, for example, a road traffic information communication system (VICS: registered trademark) and a probe car system are examples of systems for providing users with information on road congestion conditions. Here, in the above-described probe car system, based on the information collected from each vehicle, it is possible to identify traffic congestion conditions not only on main roads but also on roads in a wider range and provide the information to the user.

For example, in Japanese Unexamined Patent Application Publication No. 2015-7902, information specifying the inter-vehicle distance and the vehicle position with other vehicles calculated based on the captured image captured by each vehicle traveling on the road is collected, and the collected information A technique for specifying the vehicle density on a road (that is, the degree of congestion of the road) is disclosed by analyzing the . Also disclosed is the calculation of the vehicle-to-vehicle distance for each lane.

Japanese Patent Application Laid-Open No. 2015-7902 (pages 5-11)

Here, in Patent Document 1, the relative relationship (for example, the inter-vehicle distance) between another vehicle included in the captured image captured by the vehicle actually traveling on the road and the captured vehicle (hereinafter referred to as the captured vehicle) is captured. It collects data from vehicles and determines vehicle density on roads based on the information collected. However, the relative relationship between the imaged vehicle and other vehicles calculated from the imaged image without specifying the driving lane and vehicle speed has low reliability as information, and the vehicle density of the road calculated only from such information is also inaccurate. It may be informational. In addition, what is collected is the inter-vehicle distance between the imaging vehicle and other vehicles near the imaging vehicle at the time of imaging. It is also difficult to calculate the vehicle density of

On the other hand, there is also a method of collecting travel information (for example, vehicle speed) of vehicles actually traveling on the road and determining the traffic congestion situation. There is a problem that only the lane in which the vehicle actually traveled can be judged.

The present invention has been made to solve the above-mentioned problems in the prior art. Information based on travel information specifying the lane and vehicle speed of each vehicle traveling on the road and the imaged image of the surroundings of each vehicle. It is an object of the present invention to provide a traffic condition determination system and a traffic condition determination device capable of accurately determining traffic congestion conditions on a road for each lane in a wider range by using together.

In order to achieve the above object, the first traffic condition determination system according to the present invention comprises: a traveling information collecting means for collecting information specifying the traveling lane and vehicle speed of each vehicle traveling on the road as traveling information; Image information collecting means for collecting information about captured images of the surroundings of each vehicle captured by an imaging device provided in each running vehicle as peripheral image information; and the running information and the image information collected by the running information collecting means. traffic congestion determination means for determining a traffic congestion situation of a road for each lane based on the peripheral image information collected by the collection means, wherein the traffic congestion determination means determines traffic congestion for the same lane based on the travel information. and when traffic congestion determination based on the surrounding image information is possible, the determination result of traffic congestion determination based on the travel information is preferentially used.
A second traffic condition determination system according to the present invention includes traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road; and each vehicle traveling on the road. Image information collecting means for collecting information about captured images of the surroundings of each vehicle captured by an imaging device provided in the vehicle as peripheral image information; traffic congestion determination means for determining the traffic congestion situation of the road for each lane based on the peripheral image information obtained; map information acquisition means for acquiring map information including information on the number of lanes on the road; and the traffic congestion determination means When the number of lanes of the road on which the congestion state is determined does not match the number of lanes of the same road in the map information, the determination result of the congestion determination means is corrected so as to match the number of lanes of the same road in the map information. and determination result correcting means.
A third traffic condition determination system according to the present invention includes traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road; and each vehicle traveling on the road. Image information collecting means for collecting information about captured images of the surroundings of each vehicle captured by an imaging device provided in the vehicle as peripheral image information; traffic congestion determination means for determining a traffic congestion situation of a road for each lane based on the obtained surrounding image information; In this case, for each imaging vehicle, the congestion status of the road is determined for each lane based on the peripheral image information related to the image captured by the imaging vehicle, and the traffic congestion determination based on the plurality of imaging vehicles is performed for the same lane. If possible, priority is given to the determination result of congestion determination based on the captured vehicle traveling in a lane closer to the lane to be determined.
A fourth traffic condition determination system according to the present invention includes traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road; and each vehicle traveling on the road. Image information collecting means for collecting information about captured images of the surroundings of each vehicle captured by an imaging device provided in the vehicle as peripheral image information; traffic congestion determination means for determining a traffic congestion situation of a road for each lane based on the obtained surrounding image information; In this case, for each imaging vehicle, the congestion status of the road is determined for each lane based on the peripheral image information related to the image captured by the imaging vehicle, and the traffic congestion determination based on the plurality of imaging vehicles is performed for the same lane. If possible, priority is given to the determination result of traffic jam determination based on the imaging vehicle that entered late into the link including the lane to be determined.
A fifth traffic condition determination system according to the present invention includes traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road; and each vehicle traveling on the road. Image information collecting means for collecting information about captured images of the surroundings of each vehicle captured by an imaging device provided in the vehicle as peripheral image information; traffic congestion determination means for determining a traffic congestion situation of a road for each lane based on the obtained surrounding image information; In this case, for each imaging vehicle, the congestion status of the road is determined for each lane based on the peripheral image information related to the image captured by the imaging vehicle, and the traffic congestion determination based on the plurality of imaging vehicles is performed for the same lane. If it is possible and includes two or more different determination results, the determination result with the greater number of determined imaging vehicles is used with priority over the congestion determination determination result.
The "information for identifying the driving lane of the vehicle" may be information for identifying the driving lane itself, or may be information for identifying the driving lane (for example, positional coordinates of the vehicle).
Also, the “information about the captured image” may be the captured image itself, or may be data derived by image processing on the captured image.

In addition, a first traffic condition determination device according to the present invention includes traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road, and each vehicle traveling on the road. Image information collecting means for collecting information about captured images of the surroundings of each vehicle captured by an imaging device provided in the vehicle as peripheral image information; traffic congestion determination means for determining a traffic congestion situation of a road for each lane based on the peripheral image information that has been obtained ; When it is possible to determine congestion based on information, the determination result of congestion determination based on the travel information is preferentially used .
In addition, a second traffic condition determination device according to the present invention includes traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road, and each vehicle traveling on the road. Image information collecting means for collecting information about captured images of the surroundings of each vehicle captured by an imaging device provided in the vehicle as peripheral image information; traffic congestion determination means for determining the traffic congestion situation of the road for each lane based on the peripheral image information obtained; map information acquisition means for acquiring map information including information on the number of lanes on the road; and the traffic congestion determination means When the number of lanes of the road on which the congestion state is determined does not match the number of lanes of the same road in the map information, the determination result of the congestion determination means is corrected so as to match the number of lanes of the same road in the map information. and determination result correcting means.

According to the first , third, fourth, and fifth traffic condition determination systems and the first traffic condition determination device according to the present invention having the above configurations, based on the information collected from each vehicle traveling on the road, When judging the congestion status of a road for each lane, the driving information of a vehicle with high reliability as information but a narrow range that can be judged, and the driving information of a vehicle that has a low reliability as information but a wide judgment range. By using image-based information together, it becomes possible to accurately determine traffic congestion conditions for each lane in a wider range. In addition, even if different traffic congestion determination results overlap for the same lane, the traffic congestion determination results with high reliability as information can be preferentially used, so the traffic congestion status of the road for each lane can be determined. It becomes possible to determine more accurately.
Further, according to the second traffic condition determination system and the second traffic condition determination device according to the present invention, when determining the congestion condition of the road for each lane based on the information collected from each vehicle traveling on the road, In the above, by using together driving information of a vehicle with a high reliability as information but a narrow range that can be determined and information based on a captured image of a vehicle that has a low reliability as information but a wide range of determination, It is possible to accurately determine traffic congestion conditions for each lane in a wider range. Also, when judging the congestion status of each lane based on the information collected from each vehicle traveling on the road, the number of lanes on the road for which the congestion status is judged does not match the actual number of lanes. However, by correcting the determination result, it is possible to match the actual lane.

1 is a schematic configuration diagram showing a traffic situation determination system according to this embodiment; FIG. 1 is a block diagram showing the configuration of a traffic situation determination system according to this embodiment; FIG. It is the figure which showed an example of the probe information memorize|stored in probe information DB. FIG. 3 is a diagram showing an example of information about traffic congestion conditions for each lane stored in a traffic congestion information DB; 2 is a block diagram schematically showing a control system of the navigation device according to this embodiment; FIG. 4 is a flow chart of a traffic jam situation determination processing program according to the present embodiment; FIG. 10 is a flowchart of a sub-processing program for self-lane processing; FIG. It is a figure explaining the identification method of the lane where an imaging vehicle is located. FIG. 10 is a diagram illustrating a method of specifying a driving lane in which the imaging vehicle mainly traveled during the imaging of the captured image; FIG. 10 is a flowchart of a sub-processing program for other lane density processing; FIG. It is the figure which showed the other vehicle detected based on the captured image. FIG. 10 is a flowchart of a sub-processing program for other lane speed processing; FIG. FIG. 10 is a flowchart of a sub-processing program of lane matching processing; FIG. 4 is a flowchart of a sub-processing program for parallel running processing; FIG. 10 is a diagram showing an example of finalizing a traffic congestion determination result in a case where traffic congestion determination based on a plurality of imaging vehicles is possible for the same lane. FIG. 10 is a diagram showing an example of finalizing a traffic congestion determination result in a case where traffic congestion determination based on a plurality of imaging vehicles is possible for the same lane. FIG. 10 is a diagram showing an example of finalizing a traffic congestion determination result in a case where traffic congestion determination based on a plurality of imaging vehicles is possible for the same lane. FIG. 10 is a diagram showing an example of finalizing a traffic congestion determination result in a case where traffic congestion determination based on a plurality of imaging vehicles is possible for the same lane.

DETAILED DESCRIPTION OF THE INVENTION A traffic situation determination system according to an embodiment of the present invention will be described in detail below with reference to the drawings. First, a schematic configuration of a traffic situation determination system 1 according to this embodiment will be described with reference to FIGS. 1 and 2. FIG. FIG. 1 is a schematic configuration diagram showing a traffic situation determination system 1 according to this embodiment. FIG. 2 is a block diagram showing the configuration of the traffic situation determination system 1 according to this embodiment.

As shown in FIG. 1, a traffic condition determination system 1 according to the present embodiment includes a server device (traffic condition determination device) 3 provided in a probe center 2 and a navigation device as a communication (guidance) terminal mounted in a vehicle 4. 5 and . Also, the server device 3 and the navigation device 5 are configured to be able to transmit and receive electronic data to and from each other via a communication network 6 . Incidentally, instead of the navigation device 5, for example, a mobile phone, a smart phone, a tablet terminal, or a personal computer may be used.

Here, the traffic condition determination system 1 according to this embodiment constitutes a so-called probe car system. Here, the probe car system is a system that collects information using the vehicle 4 as a sensor. Specifically, the vehicle 4 transmits speed data, the operation status of each system such as steering operation and shift position, along with GPS position information to the probe center 2 via a communication device mounted in advance on the vehicle 4, A system that reuses the collected data as various information on the center side.

The server device 3 provided in the probe center 2 appropriately collects and accumulates probe information (material information) including the current time and travel information from each vehicle 4 that travels all over the country. (for example, road closure information, accident information, congestion information, travel time, etc.) is generated, and the generated support information is distributed to the navigation device 5, and various processes are performed using the support information. It is an information management server. In particular, in this embodiment, the server device 3 collects from each vehicle 4 the current position coordinates of the vehicle 4, the vehicle speed, and the imaged image of the surroundings captured by the vehicle-mounted camera 7 provided in the vehicle 4, and the collected information is statistically or Data indicating traffic congestion conditions for each lane is generated by the analysis, and distributed to the vehicle 4 .

Here, the vehicle-mounted camera 7 is composed of a camera using a solid-state imaging device such as a CCD, for example, and is attached to the bumper of the vehicle 4, the rear side of the rearview mirror, the door mirror, etc., and is capable of imaging the surrounding environment of the vehicle 4. The optical axis direction is set to . The number of vehicle-mounted cameras 7 may be singular or plural. For example, the vehicle-mounted camera 7 captures an image of the surrounding environment in front of the vehicle 4 while the vehicle 4 is traveling. It should be noted that the imaging range may be the side or the rear of the vehicle 4 instead of the front in the traveling direction.

On the other hand, the navigation device 5 is mounted on the vehicle 4, and displays a map around the vehicle position based on the stored map data, displays the current position of the vehicle on the map image, and guides the set guidance route. It is an in-vehicle device that guides movement along the route. In addition, the navigation device 5 also guides the user with the information about the traffic congestion situation for each lane received from the server device 3 . Details of the navigation device 5 will be described later.

The communication network 6 includes a large number of base stations located all over the country and communication companies that manage and control each base station. It is configured by connecting Here, the base station has a transceiver (transmitter/receiver) and an antenna for communicating with the navigation device 5 . While the base station performs wireless communication between communication companies, it is the terminal of the communication network 6, and relays communication with the server device 3 from the navigation device 5 within the range (cell) where the radio waves of the base station reach. have a role to play.

Note that the vehicle 4 for which information is collected and the vehicle 4 for which traffic congestion information for each lane generated based on the collected information is distributed need not be the same vehicle, and may be different vehicles. can be Also, the information regarding the traffic congestion situation for each lane does not necessarily have to be distributed to the vehicle 4, and may be distributed to, for example, a mobile phone, a smart phone, a tablet-type terminal, or a personal computer.

Next, the configuration of the server device 3 that constitutes the traffic situation determination system 1 will be described in more detail with reference to FIG.

The server device 3 basically comprises a server control ECU 11, a map information DB 12 as information recording means connected to the server control ECU 11, a probe information DB 13, a traffic jam information DB 14, and a center communication device 15, as shown in FIG. is structured.

The server control ECU 11 (electronic control unit) is an electronic control unit that controls the entire server device 3, and is used as a CPU 21 as an arithmetic device and a control device, and as a working memory when the CPU 21 performs various arithmetic processing. In addition to a RAM 22, a program for control, a ROM 23 in which a traffic jam situation determination processing program (see FIG. 6) described later, etc. is recorded, and an internal storage device such as a flash memory 24 for storing a program read out from the ROM 23. . The server control ECU 11 constitutes various means as a processing algorithm together with an ECU of the navigation device 5, which will be described later. For example, the travel information collecting means collects, as travel information, information specifying the travel lane and vehicle speed of each vehicle traveling on the road. The image information collecting means collects, as surrounding image information, information on captured images of the surroundings of each vehicle captured by an image capturing device of each vehicle traveling on the road. The traffic congestion determining means determines traffic congestion conditions of the road for each lane based on the traveling information collected by the traveling information collecting means and the peripheral image information collected by the image information collecting means.

The map information DB 12 is storage means for storing map information registered based on external input data or input operations, classified by area (for example, by 20 km square mesh). The map information DB 12 has basically the same configuration as the map information stored in the navigation device 5, which will be described later. consists of For example, it includes link data related to roads (links), node data related to node points, intersection data related to each intersection, point data related to points such as facilities, and the like. In particular, the link data includes position coordinates of both ends of the link and information on the structure of lanes included in the link (number of lanes, width of lanes, points where lanes increase or decrease if the number of lanes increases or decreases in the middle of the link, etc.).

Further, the probe information DB 13 is storage means for cumulatively storing probe information collected from each vehicle 4 traveling all over the country. In the present embodiment, as the probe information collected from the vehicle 4, (a) an image captured by the vehicle-mounted camera 7 included in the vehicle, and (b) the vehicle at the time of capturing each frame included in the captured image and (c) the link on which the vehicle is currently traveling and the time of entry into the link. However, the probe information does not necessarily include all of the information regarding (a) to (c), and may include only the information regarding (a) and (b), for example. Further, when determining the traffic congestion situation only for a specific link (for example, an approach link to an intersection), probe information may be collected only from vehicles traveling on the link.

FIG. 3 is a diagram showing an example of probe information stored in the probe information DB 13. As shown in FIG. As shown in FIG. 3, the probe information includes a vehicle ID that identifies the vehicle that is the transmission source, and information related to (a) to (c) above. For example, the probe information shown in FIG. The current position and speed of the vehicle during the imaging are stored in units of frames. Similarly, other probe information is also stored. Note that the captured image shown in FIG. 3 indicates the ID of the captured image, but if there is the vehicle ID, the traveling link, and the link entry time, the captured image can be specified, so it is not always necessary to assign the ID to the captured image. no.

On the other hand, the traffic jam information DB 14 is storage means for cumulatively storing information (distribution information) regarding traffic jam conditions for each lane generated by statistically or analyzing the probe information stored in the probe information DB 13 . Incidentally, in this embodiment, the congestion situation is judged in two stages, for example, "empty" and "congested", but it may be judged in three stages of "empty", "congested", and "congested", or in four or more stages. . Also, the traffic jam condition may be identified by the average vehicle speed or the travel time. The details of the processing for judging traffic conditions will be described later.

FIG. 4 is a diagram showing an example of information about traffic congestion conditions for each lane stored in the traffic congestion information DB 14. As shown in FIG. As shown in FIG. 4, the traffic congestion information for each lane includes information specifying the date and time when the traffic congestion was determined, information specifying the link and lane, and traffic congestion status of the determined lane "congestion"). Specifically, "statistical date and time", "link ID", "lane No." ” and “degree of congestion” correspond to each other. In addition, "Lane No. , for example, defines 1, 2, 3, . For example, with the link ID "100001", the lane No. If it is "1", it indicates the leftmost lane of the link ID "100001". In the example shown in FIG. 4, the information on the traffic congestion situation for each lane includes information on lanes judged to be "unoccupied", but only information on lanes judged to be "congested" is included. Also good. In addition, "unknown" may be stored for lanes for which the congestion status cannot be determined.

Then, the server device 3 distributes the traffic congestion information for each lane stored in the traffic congestion information DB 14 to the navigation device 5 in response to a request from the navigation device 5 . On the other hand, the navigation device 5, to which the traffic congestion information for each lane has been distributed, guides the user to the distributed traffic congestion status for each lane, and searches for a route to the destination in consideration of the distributed traffic congestion status. .

The center communication device 15 is a communication device for communicating with the vehicle 4 or an external traffic information center such as a VICS (registered trademark: Vehicle Information and Communication System) center via the communication network 6 . In this embodiment, probe information and distribution information are transmitted and received to and from each vehicle 4 via the center communication device 15 .

Next, a schematic configuration of the navigation device 5 mounted on the vehicle 4 will be described with reference to FIG. FIG. 5 is a block diagram showing the navigation device 5 according to this embodiment.

As shown in FIG. 5, the navigation device 5 according to the present embodiment includes a current position detection unit 31 for detecting the current position of the vehicle in which the navigation device 5 is mounted, a data recording unit 32 for recording various data, A navigation ECU 33 that performs various types of arithmetic processing based on input information, an operation unit 34 that receives operations from the user, and a liquid crystal display 35 that displays a map of the vehicle's surroundings and information about traffic congestion to the user. , a speaker 36 for outputting voice guidance related to route guidance, a DVD drive 37 for reading a DVD as a storage medium, and a communication module 38 for communicating with information centers such as the probe center 2 and the VICS center. ing. In addition, the navigation device 5 is connected via an in-vehicle network such as CAN to an in-vehicle camera 7 installed in the vehicle on which the navigation device 5 is mounted.

Each component constituting the navigation device 5 will be described in order below.
The current position detection unit 31 includes a GPS 41, a vehicle speed sensor 42, a steering sensor 43, a gyro sensor 44, etc., and is capable of detecting the current vehicle position, direction, vehicle speed, current time, and the like. . In particular, the vehicle speed sensor 42 is a sensor for detecting the moving distance and speed of the vehicle, generates a pulse according to the rotation of the driving wheels of the vehicle, and outputs the pulse signal to the navigation ECU 33 . Then, the navigation ECU 33 calculates the rotational speed of the drive wheels and the movement distance by counting the generated pulses. It should be noted that the navigation device 5 does not have to include all of the above four types of sensors, and the navigation device 5 may include only one or more of these sensors.

In addition, the data recording unit 32 reads a hard disk (not shown) as an external storage device and a recording medium, a map information DB 45, a travel history DB 46, a distribution information DB 47, a predetermined program, etc. recorded in the hard disk, and stores them in the hard disk. It has a recording head (not shown) which is a driver for writing predetermined data. Note that the data recording unit 32 may be configured by a flash memory, a memory card, or an optical disk such as a CD or DVD instead of the hard disk. Further, the map information DB 45, the travel history DB 46, and the distribution information DB 47 may be stored in an external server, and the navigation device 5 may acquire them through communication.

Here, the map information DB 45 includes, for example, link data related to roads (links), node data related to node points, search data used for processing related to search and change of routes, facility data related to facilities, maps for displaying maps, and so on. This is storage means for storing display data, intersection data for each intersection, search data for searching for points, and the like.

Further, the travel history DB 46 is a storage unit that accumulates and stores the travel information of the vehicle 4 and the captured images captured by the vehicle-mounted camera 7 . In this embodiment, the position coordinates and vehicle speed of the vehicle at the time of capturing each frame included in the captured image are stored. The current position of the vehicle 4 may be detected using the GPS 41 or the vehicle speed sensor 42, but it is desirable to specify it in detail using a high-accuracy location technique. The travel information and captured images stored in the travel history DB 46 are transmitted to the server device 3 at any time as probe information.

Also, the distribution information DB 47 is storage means for storing distribution information (information on traffic jam conditions for each lane) distributed from the server device 3 .

On the other hand, a navigation ECU (electronic control unit) 33 is an electronic control unit that controls the entire navigation device 5, and includes a CPU 51 as an arithmetic device and a control device, and a working memory when the CPU 51 performs various arithmetic processing. A RAM 52 that stores route data and the like when a route is searched, a ROM 53 that stores control programs and the like, and an internal storage device such as a flash memory 54 that stores programs read from the ROM 53. I have.

The operation unit 34 is operated when inputting a departure point as a travel start point and a destination as a travel end point, and is composed of a plurality of operation switches (not shown) such as various keys and buttons. Then, the navigation ECU 33 performs control to execute various corresponding operations based on switch signals output by pressing of each switch or the like. The operation unit 34 can also be configured by a touch panel provided on the front surface of the liquid crystal display 35 . It can also be composed of a microphone and a voice recognition device.

In addition, the liquid crystal display 35 displays a map image including roads, traffic information, operation guidance, operation menu, key guidance, guidance information along the guidance route (planned driving route), news, weather forecast, time, mail, TV Programs, etc. are displayed. In addition, in the present embodiment, especially when providing traffic congestion information, guidance is provided regarding traffic congestion conditions for each lane. For example, the congested lanes and the other lanes are displayed on the liquid crystal display 35 in different colors. A HUD or HMD may be used instead of the liquid crystal display 35 .

In addition, the speaker 36 outputs voice guidance for driving along a guidance route (planned driving route) and traffic information guidance based on instructions from the navigation ECU 33 . In addition, in the present embodiment, especially when providing traffic congestion information, guidance is provided regarding traffic congestion conditions for each lane. For example, it outputs a voice guidance saying that "the left (center and right) lanes are congested".

Also, the DVD drive 37 is a drive capable of reading data recorded on recording media such as DVDs and CDs. Then, based on the read data, music and video are reproduced, the map information DB 45 is updated, and so on. A card slot for reading and writing a memory card may be provided instead of the DVD drive 37 .

Also, the communication module 38 is a communication device for receiving traffic information, weather information, etc. transmitted from a traffic information center, for example, a VICS center or other external center, and corresponds to, for example, a mobile phone or a DCM. It also includes a vehicle-to-vehicle communication device that communicates between vehicles and a road-to-vehicle communication device that communicates with a roadside unit. It is also used to transmit and receive probe information and distribution information to and from the server device 3 .

Next, a traffic jam situation determination processing program executed by the server device 3 included in the traffic situation determination system 1 having the above configuration will be described with reference to FIG. FIG. 6 is a flow chart of a traffic jam situation determination processing program according to the present embodiment. Here, the congestion status determination processing program is executed after a predetermined time (for example, one minute) has elapsed since the previous execution of the program, and based on the probe information transmitted from each vehicle 4, the congestion status of each lane is determined. It's a program. 6, 7, 10, and 12 to 14 are stored in the RAM 22, ROM 23, etc. of the server device 3 and executed by the CPU 21.

First, in step (hereinafter abbreviated as S) 1, the CPU 21 reads probe information newly collected from each vehicle from the time of the previous processing to the time of the current processing (that is, within the last 1 minute) from the probe information DB 13. . The probe information collected from the vehicle includes "captured image captured by the vehicle-mounted camera 7 provided in the vehicle", "positional coordinates and vehicle speed of the vehicle at the time of capturing each frame included in the captured image", and "vehicle The current link and the entry time to the link are included (see FIG. 3). It should be noted that the "link on which the vehicle is currently running" may not be included in the probe information. Even in that case, by performing map matching processing using the position coordinates of the vehicle included in the probe information and the map information held by the server device 3, it is possible for the server device 3 to identify the link on which the vehicle travels. be.

The probe information may be distributed to the server device 3 at the timing when the vehicle reaches the end point of the link, for example, while the vehicle is traveling on the link. interval), and the probe information acquired during that period may be distributed to the server device 3 .

Next, in S2, the CPU 21 acquires intersection information. Note that S2 is executed only when judging the traffic congestion situation only for the approach link to a specific intersection, and acquires the position information of the target intersection, the link ID of the link connected to the intersection, and the like. On the other hand, if the target of traffic congestion determination is not limited to the entry link to a specific intersection (for example, if all links nationwide are targeted), the processing of S2 is unnecessary.

Subsequently, in S3, the CPU 21 acquires link information of a link for which traffic congestion status is to be determined. Specifically, it acquires information about the positional coordinates of both ends of the link, the structure of the lanes included in the link (the number of lanes, the width of the lanes, the points where the lanes increase or decrease if there is an increase or decrease in the middle of the link, etc.). Note that if the target of traffic congestion determination is not limited to a specific link (that is, if all links nationwide are targeted), link information about all links nationwide is acquired. However, it is also possible to acquire only the link information of the "link on which the vehicle is currently running" included in the probe information acquired in S1.

After that, in S4, the CPU 21 performs a process of dividing the data in order to shorten the processing time for the probe information acquired in S1. The number of divisions is, for example, 2, but may be divided into 3 or more. Also, when dividing, the difference in the data amount of each piece of data after division is made as small as possible.

For example, data is divided by the following processing.
First, the number of data (corresponding to the number of frames of captured images) is counted for each vehicle for which probe information has been collected (hereinafter referred to as an imaging vehicle), and the imaging vehicles are sorted in ascending order of the number of data. Then, the probe information acquired in S1 is divided into the probe information collected from the odd-numbered imaging vehicles and the probe information collected from the even-numbered imaging vehicles. Thereafter, processing is performed on the divided probe information. However, the process of S4 may be omitted.

Next, in S5, the CPU 21 executes self-lane processing (FIG. 7), which will be described later. The self-lane process is a process for judging the congestion status of the lane in which the imaging vehicle travels based on the traveling information of the imaging vehicle, among the probe information acquired in S1, as will be described later.

Subsequently, in S6, the CPU 21 executes other lane density processing (FIG. 10), which will be described later. In addition, the other lane density processing is performed by performing image processing on the captured image captured by the imaging vehicle, among the probe information acquired in S1 as described later, so that the vehicle density in the lane other than the lane in which the imaging vehicle traveled. is calculated, and the congestion status of the lane is determined from the calculated vehicle density.

Further, in S7, the CPU 21 executes other lane speed processing (FIG. 12), which will be described later. In the other lane speed processing, among the probe information acquired in S1, as described later, image processing is performed particularly on the captured image captured by the imaging vehicle, so that the vehicle can travel in a lane other than the lane in which the imaging vehicle has traveled. This is a process of calculating the traveling speed of the other vehicle and determining the congestion status of the lane in which the other vehicle is traveling from the calculated traveling speed of the other vehicle.

After that, in S8, the CPU 21 performs data integration processing. In the data integration process, the traffic congestion determination results of S5 to S7 are classified for each link and each lane, and the traffic congestion status determination results for the same link and the same lane are linked (integrated). .

Subsequently, in S9, the CPU 21 executes lane matching processing (FIG. 13), which will be described later. Note that the lane matching process is a process of comparing the number of lanes for which congestion conditions have been determined in S5 to S7 and the number of lanes on the actual map information, as described later, and determining whether or not they match. be.

Further, in S10, the CPU 21 executes parallel running processing (FIG. 14), which will be described later. In the parallel running process, when there are two or more traffic congestion judgment results for the same link and same lane as described later, the final traffic congestion situation is determined using the judgment priority and the like. processing. Then, by performing the parallel running process, the final congestion situation for each lane is specified.

After that, in S11, the CPU 21 stores in the traffic congestion information DB 14 the information on the traffic congestion situation for each lane finally specified as a result of the parallel running processing in S10.

Then, in S12, the CPU 21 distributes the traffic congestion information for each lane stored in the traffic congestion information DB 14 to the navigation device 5 of the vehicle 4 that has made the request. It should be noted that the information may be distributed to the navigation devices 5 of all vehicles 4 that are in a communicable state regardless of the presence or absence of a distribution request. Also, the information regarding the traffic congestion situation for each lane does not necessarily have to be distributed to the vehicle 4, and may be distributed to, for example, a mobile phone, a smart phone, a tablet-type terminal, or a personal computer.

The navigation device 5 to which the traffic congestion information for each lane is distributed can guide the user about the distributed traffic congestion status for each lane and search for a route to the destination in consideration of the distributed traffic congestion status. becomes.

Next, the sub-processing of the self-lane processing executed in S5 will be described with reference to FIG. FIG. 7 is a flow chart of a sub-processing program for self-lane processing.

The subsequent processing of S21 to S28 is performed for each link subject to determination of the traffic congestion situation and for each imaging vehicle from which probe information has been collected in S1. Further, the processing of S21 to S23 is performed for each frame included in the captured image captured by the imaging vehicle to be processed on the link to be processed. Then, after the processing of S21 to S28 is completed for all the relevant links and imaging vehicles, the process proceeds to S6.

First, in S21, the CPU 21 counts up the number N of frames. Note that the initial value of the number of frames N is set to 0, and is initialized when the process of S27 or S28 is executed. The counted-up current frame number N is stored in the flash memory 24 or the like.

Next, in S22, the CPU 21 extracts the vehicle speed at the time when the frame to be processed is captured for the imaging vehicle to be processed from among the probe information acquired in S1, and the extracted vehicle speed value is added to the vehicle speed addition value S. to add. The initial value of the vehicle speed addition value S is set to 0, and is initialized when the process of S27 or S28 is executed. The added current vehicle speed additional value S is stored in the flash memory 24 or the like.

Subsequently, in S23, the CPU 21 extracts the position coordinates of the imaging vehicle to be processed from the probe information acquired in S1 at the time of capturing the frame to be processed. After that, by matching the extracted position coordinates with the map information provided in the server device 3 on a lane basis, the lane in which the imaging vehicle to be processed is located at the time of capturing the frame to be processed is specified. The map information includes information specifying the structure of lanes in the link. For example, as shown in FIG. 8, when the imaging vehicle travels on a link consisting of two lanes 61 and 62, when the position coordinates 63 match on the left lane 61, the imaging vehicle captures the frame to be processed. The vehicle is identified as being in the lane 61 at the time, and the lane 61 is counted up. Note that the count-up value for each lane is initialized when the process of S27 or S28 is executed. The current value counted up for each lane is stored in the flash memory 24 or the like.

Then, after performing the processing of S21 to S23 on all the frames included in the captured image captured by the imaging vehicle to be processed in the link to be processed, the process proceeds to S24.

At S24, the CPU 21 reads out the final vehicle speed addition value S added at S22 from the flash memory 24 and divides it by the final frame number N counted at S21. As a result, the average vehicle speed during the imaging of the imaging vehicle to be processed is calculated.

Next, at S25, the CPU 21 reads the count value for each lane counted at S23 from the flash memory 24, and specifies the lane with the highest count value. As a result, the lane in which the imaging vehicle to be processed is mainly traveling during the imaging of the captured image (hereinafter referred to as the driving lane) is specified. For example, as shown in FIG. 9, when the imaging vehicle travels on a link consisting of two lanes 61 and 62, if the position coordinates 63 match the left lane 61 and the right lane 62, respectively, the right lane Since the lane 62 has been matched more times, the lane 62 is specified as the driving lane in which the imaging vehicle to be processed mainly traveled during the imaging of the captured image.

Subsequently, in S26, the CPU 21 determines whether or not the average vehicle speed calculated in S24 is equal to or higher than the threshold. The threshold value may be a fixed value (for example, 10 km/h) or may be changed depending on the road type (for example, 40 km/h for expressways and 10 km/h for general roads).

And when it determines with the average vehicle speed calculated by said S24 being more than a threshold value (S26:YES), it transfers to S27. On the other hand, when it is determined that the average vehicle speed calculated in S24 is less than the threshold value (S26: NO), the process proceeds to S28.

In S27, the CPU 21 determines that the congestion status of the lane in which the imaging vehicle specified in S25 among the lanes included in the link to be processed is "empty (no congestion)".

On the other hand, in S28, the CPU 21 determines that the traffic jam condition of the lane in which the imaging vehicle is traveling specified in S25 is "traffic jam" among the lanes included in the link to be processed. The determination results of S27 and S28 are temporarily stored in the flash memory 24. FIG.

After that, after the processing of S21 to S28 is completed for all the relevant links and imaging vehicles, the process proceeds to S6. In other words, the traffic lane is specified for each link and for each image pickup vehicle, and congestion determination of the traffic lane is performed.

Next, the sub-processing of the other lane density processing executed in S6 will be described with reference to FIG. FIG. 10 is a flow chart of a sub-processing program for other lane density processing.

The subsequent processes of S31 to S37 are performed for each link subject to determination of the traffic jam situation and for each imaging vehicle for which probe information has been collected in S1. Then, after the processing of S31 to S37 is completed for all the relevant links and imaging vehicles, the process proceeds to S7.

First, in S<b>31 , the CPU 21 calculates the traveling distance of the imaging vehicle to be processed on the link to be processed. The link length of the link to be processed may be regarded as the traveling distance of the imaging vehicle, or the actual traveling distance may be calculated from the history of the position coordinates of the imaging vehicle included in the probe information.

Note that the subsequent processes of S32 and S33 are performed for each vehicle (hereinafter referred to as another vehicle) included in the imaged image captured by the imaged vehicle to be processed on the link to be processed. However, other vehicles located on the driving lane of the imaging vehicle are excluded from the processing targets. In addition, detection of other vehicles included in the imaging vehicle is performed by, for example, the following method.

First, the CPU 21 performs image processing on each frame included in the captured image, and detects other vehicles included in each frame. It should be noted that the detection of other vehicles from the image is performed, for example, by matching processing using feature points. After that, the identity of other vehicles is determined from the shape and color of the vehicle detected between the preceding and following frames, and the same ID is given to the same other vehicles. "Number of assigned IDs"="Number of detected other vehicles", and the subsequent processes of S32 and S33 are performed for each other vehicle to which an ID is assigned (that is, for each ID of another vehicle).

For example, in the example shown in FIG. 11, in a link consisting of three lanes, it is a diagram showing an example of a detection mode of other vehicles when the imaging vehicle 65 travels in the center lane. In the example shown in FIG. 11, while the vehicle is traveling on the link, first, at point A, other vehicles are detected in the left lane and the right lane, respectively. For example, an ID "0001" is assigned to another vehicle detected in the left lane, and an ID "1001" is assigned to another vehicle detected in the right lane. After that, when the imaging vehicle 65 reaches the point B, the other vehicle with the ID "1001" detected in the right lane temporarily becomes undetected. Another vehicle is detected. For example, ID "1002" is assigned to a new other vehicle detected in the right lane. Meanwhile, another vehicle with ID "0001" is continuously detected in the left lane. After that, when the imaging vehicle 65 reaches point D, the other vehicle with ID "0001" detected in the left lane and the other vehicle with ID "1002" detected in the right lane temporarily become non-detected. After that, when the point E is reached, a new other vehicle is detected in the left lane. For example, ID "0002" is assigned to a new other vehicle detected in the left lane. After that, when the imaging vehicle 65 reaches the point F, another new vehicle is detected in the right lane. For example, ID "1003" is assigned to a new other vehicle detected in the right lane. Next, when the imaging vehicle 65 reaches point G, another vehicle with ID "1003" detected in the right lane temporarily becomes non-detected, and when it reaches point H, it is detected in the left lane. The other vehicle with the ID "0002" that had been detected is also in the non-detection state. In the example shown in FIG. 11, as a result, two other vehicles with IDs "0001" and "0002" are detected in the left lane, and three other vehicles with IDs "1001", "1002" and "1003" are detected in the right lane. Other vehicles on the platform will be detected.

Then, in S32, the CPU 21 determines whether or not the vehicle speed of the imaging vehicle when the other vehicle to be processed is detected is equal to or less than a predetermined speed. Note that the vehicle speed while the imaging vehicle is capturing the captured image is included in the probe information acquired in S1. Further, the predetermined speed used as the criterion in S32 may be a fixed value (for example, 5 km/h), or may be changed depending on the road type (for example, 20 km/h for expressways and 5 km/h for general roads). .

Then, if it is determined that the vehicle speed of the imaging vehicle when the other vehicle to be processed is detected is faster than the predetermined speed (S32: NO), the process proceeds to S33.

In S33, the CPU 21 counts up the number T of the other vehicles detected by classifying them for each lane. The initial value of the number T of other vehicles is set to 0, and is initialized when the process of S36 or S37 is executed. The counted-up current number T of other vehicles is classified for each lane and stored in the flash memory 24 or the like.

On the other hand, if it is determined that the vehicle speed of the imaging vehicle when the other vehicle to be processed is detected is equal to or lower than the predetermined speed (S32: YES), the imaging vehicle is stopped waiting for a traffic signal, or the like. It is presumed that there is a situation, and it is recognized that it is difficult to accurately count the number of other vehicles from the captured image. Therefore, the other vehicle to be processed is changed without counting up in S33, and the process returns to S32.

Next, in S34, the CPU 21 reads out the final number T of other vehicles counted for each lane as a result of the processing of S32 and S33. Then, the read number T of other vehicles is divided by a value corresponding to the traveled distance calculated in S31 (for example, a value obtained by dividing the traveled distance [m] by 100, and if it is 500 m, it is 5). , the number of other vehicles per unit distance (for example, 100 m) for each lane for the link to be processed, that is, the density of other vehicles for each lane is calculated. The density of other vehicles for each lane calculated in S34 is one of the parameters for specifying the traffic flow for each lane. Also, traffic flow indicates the flow of vehicles traveling on a road (including speed and density).

Subsequently, in S35, the CPU 21 determines for each lane whether or not the number of other vehicles per unit distance calculated in S34 is greater than or equal to a threshold value. The threshold value may be a fixed value (for example, 20 vehicles/100m) or may be changed depending on the road type (for example, 10 vehicles/100m for highways and 20 vehicles/100m for general roads).

When it is determined that the number of other vehicles per unit distance calculated in S34 is equal to or greater than the threshold value (S35: YES), the process proceeds to S36. On the other hand, when it is determined that the number of other vehicles per unit distance calculated in S34 is less than the threshold value (S35: NO), the process proceeds to S37.

In S36, the CPU 21 determines that the traffic congestion condition of the lane included in the link to be processed, for which the number of other vehicles per unit distance is equal to or greater than the threshold, is "congested".

On the other hand, in S37, the CPU 21 determines that the traffic congestion condition of the lane included in the link to be processed for which the number of other vehicles per unit distance is less than the threshold is "empty (no congestion)". . The processing of S35 to S37 is performed for all lanes included in the link to be processed (except for lanes not included in the captured image and lanes in which the captured vehicle travels). It is temporarily stored in the flash memory 24 .

After that, after the processing of S31 to S37 is completed for all relevant links and imaging vehicles, the process proceeds to S7. In other words, traffic jam determination for lanes other than the driving lane is performed based on the captured image for each link and for each captured vehicle.

Next, the sub-processing of the other lane speed processing executed in S7 will be described with reference to FIG. FIG. 12 is a flow chart of a sub-processing program for other lane speed processing.

Subsequent processing of S41 to S50 is performed for each link subject to determination of traffic congestion and for each imaging vehicle from which probe information has been collected in S1. Furthermore, the subsequent processing of S41 to S45 is performed for each frame included in the captured image and for each other vehicle detected in each frame. However, other vehicles located on the driving lane of the imaging vehicle are excluded from the processing targets. It should be noted that the detection of other vehicles from the captured image is performed, for example, by matching processing using feature points. Then, after the processing of S41 to S50 is completed for all the relevant links and imaging vehicles, the process proceeds to S8.

First, in S41, the CPU 21 calculates the distance from the imaging vehicle to the other vehicle at the time of imaging the frame to be processed. Note that the distance from the imaging vehicle to the other vehicle may be a straight line distance, or may be a distance along the traveling direction of the road. In addition, the distance from the imaging vehicle to the other vehicle is calculated based on the position of the other vehicle specified by specifying the position of the other vehicle in the frame by performing image processing on the frame to be processed. be. Further, when calculating the distance along the traveling direction of the road, it is desirable to correct the captured image to a top view by ortho-transforming the image before calculating.

Next, in S42, the CPU 21 determines whether or not the distance from the imaging vehicle to the other vehicle at the time of imaging the frame to be processed is equal to or less than a predetermined distance. It should be noted that the predetermined distance used as the criterion in S42 is the upper limit distance within which the error falls within an allowable range when calculating the relative speed of the other vehicle with respect to the captured vehicle from the captured image in S44, which will be described later. .

Then, when it is determined that the distance from the imaging vehicle to the other vehicle at the time of imaging the frame to be processed is equal to or less than the predetermined distance (S42: YES), the process proceeds to S43. On the other hand, when it is determined that the distance from the imaging vehicle to the other vehicle at the time of imaging the frame to be processed is longer than the predetermined distance (S42: NO), the relative speed of the other vehicle can be accurately calculated from the captured image. It is recognized that it is difficult to calculate. Therefore, the other vehicle and frame to be processed are changed without performing the processes of S43 to S45, which will be described later, and the process returns to S41.

In S43, the CPU 21 calculates the average distance to other vehicles. Specifically, among the frames imaged before the frame to be processed, a predetermined number (for example, one or two) of the frames with the latest imaging time are targeted to the other vehicle calculated in S41. Each distance is read, and the average value of the distance calculated in S41 this time and the read distance is calculated as the average distance to the other vehicle.

Next, in S44, the CPU 21 calculates the relative speed of the other vehicle with respect to the imaging vehicle at the time of imaging the frame to be processed. Specifically, D(f+1) is the distance from the imaging vehicle to the other vehicle at the time of capturing the frame to be processed, and D(f+1) is the distance from the imaging vehicle to the other vehicle at the time of capturing the previous frame. f) Assuming that the frame rate is 1/Δt, the relative velocity Va of the other vehicle with respect to the imaging vehicle is calculated by the following equation (1). D(f+1) and D(f) are preferably the average distances calculated in S43 Va=(D(f+1)-D(f))/Δt ( 1)

After that, in S45, the CPU 21 calculates the absolute speed of the other vehicle at the time of capturing the frame to be processed. Specifically, if the absolute velocity of the imaging vehicle at the time of capturing the frame to be processed is V(f+1), the absolute velocity v(f+1) of the other vehicle at the time of capturing the frame to be processed is It is calculated by the following formula (2).
v(f+1)=V(f+1)+Va (2)

After the absolute velocities of all the other vehicles in the frames are calculated for all the frames included in the captured image, the process proceeds to S46.

In S46, the CPU 21 calculates the traveling distance of the imaging vehicle to be processed on the link to be processed. The link length of the link to be processed may be regarded as the traveling distance of the imaging vehicle, or the actual traveling distance may be calculated from the history of the position coordinates of the imaging vehicle included in the probe information.

In S47, the CPU 21 calculates, for each lane included in the link to be processed, the average vehicle speed of other vehicles traveling in the lane. Specifically, first, the CPU 21 divides the absolute velocities calculated for each other vehicle and for each frame in the above S45 into units of other vehicles to which the same ID is assigned, and adds them. divided by the number of frames Thereby, the average vehicle speed for each other vehicle is calculated. Note that assignment of IDs to other vehicles is the same as the above-described other lane density processing (FIG. 10), so a description thereof will be omitted (see FIG. 11). After that, each lane is classified, and the average vehicle speed of other vehicles traveling in the lane is added and divided by the number of other vehicles. Thereby, the average vehicle speed of other vehicles traveling in the lane is calculated for each lane. Note that the average vehicle speed of other vehicles for each lane calculated in S47 is one of the parameters for specifying the traffic flow for each lane.

Subsequently, in S48, the CPU 21 determines for each lane whether or not the average vehicle speed of the other vehicles calculated in S47 is equal to or higher than the threshold. The threshold value may be a fixed value (for example, 10 km/h) or may be changed depending on the road type (for example, 40 km/h for expressways and 10 km/h for general roads).

Then, when it is determined that the average vehicle speed of the other vehicle calculated in S47 is equal to or higher than the threshold value (S48: YES), the process proceeds to S49. On the other hand, if it is determined that the average vehicle speed of other vehicles calculated in S47 is less than the threshold value (S48: NO), the process proceeds to S50.

In S49, the CPU 21 determines that the traffic congestion condition of the lane included in the link to be processed, in which the average vehicle speed of other vehicles is determined to be equal to or greater than the threshold value, is "empty (non-traffic)".

On the other hand, in S50, the CPU 21 determines that the congestion state of the lane included in the link to be processed, in which the average vehicle speed of other vehicles is determined to be less than the threshold value, is "congested". Note that the processing of S48 to S50 is performed for all lanes included in the link to be processed (excluding lanes not included in the captured image, lanes in which other vehicles do not exist, and lanes in which the captured vehicle travels). The determination result is temporarily stored in the flash memory 24 .

After that, after the processing of S41 to S50 is completed for all relevant links and imaging vehicles, the process proceeds to S8. In other words, traffic jam determination for lanes other than the driving lane is performed based on the captured image for each link and for each captured vehicle.

Next, a sub-process of the lane matching process executed in S9 will be described with reference to FIG. FIG. 13 is a flow chart of a sub-processing program for lane matching processing.

The subsequent processing of S51 and S52 is performed for each link to be determined for the traffic congestion situation. Then, after the processing of S51 and S52 is completed for all applicable links, the process proceeds to S10.

First, in S51, the CPU 21 counts the number of traffic lanes in the link to be processed that have been determined to be congested in S5 to S7. Next, the actual number of lanes of the road corresponding to the link to be processed is read from the map information held by the server device 3 . The map information includes in advance information specifying the number of lanes on the road for each link, and the actual number of lanes means the number of lanes included in the map information. Then, it is determined whether or not the number of lanes determined to be congested in S5 to S7 matches the actual number of lanes read from the map information or is smaller than the actual number of lanes. It should be noted that not all lanes of the road are included in the captured image captured by the imaging vehicle. It can occur when the number of lanes is less than the number of lanes.

Then, the number of lanes determined to be congested in S5 to S7 described above matches the actual number of lanes read from the map information, or the actual number of lanes is read from the map information. If it is determined to be less (S51: YES), the process returns to S51 after switching the link to be processed. On the other hand, when it is determined that the number of lanes for which the traffic congestion status has been determined in the above-described S5 to S7 is greater than the actual number of lanes read from the map information (S51: NO), the traffic congestion status is determined. It can be inferred that a lane that should not exist originally was recognized for some reason when making the determination. Therefore, in S52, the CPU 21 performs a correction to delete the judgment results of lanes that do not correspond to the actual lanes in order to match the actual number of lanes, that is, the number of lanes included in the map information. Specifically, for example, when the number of lanes included in the map information is 3 and the number of lanes for which the traffic congestion status is determined is 4, the number of lanes included in the map information is 3. 3 lanes to accommodate. In addition to deleting the number of lanes to match the number of lanes included in the map information, it is also possible to judge the traffic congestion situation without judging the congestion situation of this link or excluding only the data. .

Next, a sub-process of the parallel running process executed in S10 will be described with reference to FIG. FIG. 14 is a flow chart of a sub-processing program for parallel running processing.

The subsequent processing of S61 to S68 is performed for each link (hereinafter referred to as a traffic congestion determination link) including a lane (hereinafter referred to as a traffic congestion determination lane) determined to be "congested" by one of the traffic congestion determinations in S5 to S7, and Perform judgment for each lane. Then, after the processing of S61 to S68 is completed for all the traffic congestion determination links and traffic congestion determination lanes, the process proceeds to S11.

First, in S61, the CPU 21 initializes each parameter of the congestion flag, the temporary flag, and the distance L between lanes. Each parameter of the congestion flag, the temporary flag, and the distance L between lanes is stored in the flash memory 24 or the like.

The subsequent processing of S62 to S67 is performed for each driving lane of the imaging vehicle that has traveled the congestion determination link to be processed. It should be noted that the driving lane of the imaging vehicle is specified in the self-lane processing (FIG. 7) described above.

In S62, the CPU 21 calculates the distance between the processing target driving lane and the processing target congestion determination lane. Then, the calculated distance is substituted as the distance L between lanes. Note that the lane distance L is calculated with the distance between adjacent lanes set to "1". That is, if the processing target driving lane and the processing target congestion determination lane are adjacent to each other, the lane distance L is "1". Further, when only 1 other lane is sandwiched between the processing target driving lane and the processing target traffic congestion determination lane, the lane distance L is "2".

Next, in S63, the CPU 21 determines that the traffic congestion judgment lane to be processed is "congested" by traffic congestion judgment (S5 to S7) using the travel information or the captured image of the imaged vehicle traveling in the traffic lane to be processed. determine whether or not there is It should be noted that, in the traffic congestion determination processing of S5 to S7, traffic congestion determination is performed for each imaging vehicle.

Then, when the traffic congestion determination lane to be processed is determined to be "traffic" by traffic congestion determination (S5 to S7) using the travel information or the captured image of the imaged vehicle traveling in the traffic lane to be processed (S63: YES ), "congestion" is substituted for the temporary flag (S64). On the other hand, the congestion determination lane to be processed is determined to be "empty (no congestion)" by traffic congestion determination (S5 to S7) using the travel information or the captured image of the captured vehicle traveling in the lane to be processed. If there is (S63: NO), "empty (no congestion)" is substituted for the temporary flag (S65).

After that, in S66, the CPU 21 reads out the current inter-lane distance L substituted in S62, and the current inter-lane distance L is equal to or less than the minimum value of the inter-lane distance L substituted from the initialization in S61 to the present time. Determine whether or not

If it is determined that the current inter-lane distance L is equal to or less than the minimum value of the inter-lane distance L assigned from the initialization in S61 to the present time (S66: YES), the congestion flag is set to the current temporary flag. (congestion or vacancy) is substituted (S67).

On the other hand, if it is determined that the current inter-lane distance L is greater than the minimum value of the inter-lane distance L that has been assigned since the initialization in S61 until the present time (S66: NO), the congestion flag is changed. without returning to S62.

Then, after the processing of S62 to S67 is performed for the traveling lanes of all imaging vehicles that have traveled the traffic jam determination link to be processed, the process proceeds to S68.

In S68, the congestion status is finalized based on the content of the current congestion flag for the traffic congestion determination lane to be processed. Therefore, if the congestion flag is "empty (non-congested)" at the time of S68, even if the traffic congestion determination lane is once determined to be congested in S5 to S7, it will eventually become "empty (non-congested)". is confirmed.

After that, after the processing of S61 to S68 is completed for all the traffic congestion determination links and traffic congestion determination lanes, the process proceeds to S11.

As a result of performing the parallel running process shown in FIG. 14, when it is possible to determine congestion based on the travel information and congestion determination based on the captured image information for the same lane, the determination result of the congestion determination based on the travel information is given priority. It will be used for For example, as shown in FIG. 15, two imaging vehicles 65 and 66 travel in the center lane and the right lane of the same link, respectively, and the imaging vehicle 65 drives its own vehicle based on the travel information. When the center lane is determined to be "congested" and the imaging vehicle 66 determines that the center lane located on the left side of the own vehicle is "not congested" based on the captured image, the determination result of the imaging vehicle 65 is given priority. It will be done. That is, the center lane is determined to be "congested".

In addition, as a result of performing the parallel running process shown in FIG. 14, when it is possible to determine traffic jams in the same lane based on a plurality of imaged vehicles, the imaged vehicle traveling in a lane closer to the lane to be determined is determined. The judgment result of traffic jam judgment based on is preferentially used. For example, as shown in FIG. 16, two imaging vehicles 65 and 66 travel in the leftmost lane and the rightmost lane of the same link, respectively, and the imaging vehicle 65 self-determines based on the captured image. The second lane from the left located on the left side of the vehicle in which the car is traveling is determined to be "not congested", and the imaging vehicle 66 determines the second lane from the left located on the left side of the own vehicle based on the captured image to be "congested". , the determination result of the imaging vehicle 65 traveling in a lane close to the second lane from the left is prioritized. That is, the second lane from the left is determined as "non-traffic".

Further, when it is possible to determine congestion based on a plurality of imaging vehicles for the same lane, it is also possible to preferentially use the determination result of congestion determination based on imaging vehicles that entered the link including the lane to be determined late. good. For example, as shown in FIG. 17, two imaging vehicles 65 and 66 are traveling in the leftmost lane and the second lane from the right side of the same link. The second lane from the left located on the left side of the vehicle on which the vehicle is traveling is determined to be "non-congested", and the imaging vehicle 66 determines the second lane from the left located on the left side of the vehicle on the basis of the captured image. When it is determined that there is a "traffic jam", priority is given to the determination result of the imaging vehicle 65 that entered the link later. That is, the second lane from the left is determined as "non-traffic".

In addition, when it is possible to determine congestion based on a plurality of imaged vehicles for the same lane, and two or more different determination results are included, the determination result for which the number of determined imaged vehicles is larger is the determination result of the congestion determination. may be used in preference to For example, as shown in FIG. 18, three imaging vehicles 65, 66, and 67 travel on the same link, and the imaging vehicle 65 moves in the second lane from the left based on the captured image. Based on the captured image, the imaging vehicle 66 determines that the second lane from the left is "traffic." , there are more imaging vehicles determined to be "congested" than imaging vehicles determined to be "not congested", so the determination result of the imaging vehicles 66 and 67 determined to be "congested" has priority. It will be done. That is, the second lane from the left is determined to be "congested".

Further, instead of adopting the determination result with the highest priority among the determination results of the congestion determination performed in S5 to S7, the weighting of the congestion determination may be set according to the degree of priority. That is, the higher the priority of the determination result, the heavier the weighting is set. As a result, when there are many determination results, it is possible to determine a more appropriate determination result by considering those many determination results.

As described in detail above, the traffic situation determination system 1 and the server device 3 according to the present embodiment collect information specifying the driving lane and vehicle speed of each vehicle 4 traveling on the road as driving information (S1). , collects as peripheral image information (S1) information related to captured images of the periphery of each vehicle captured by an in-vehicle camera 7 provided for each vehicle 4 traveling on the road, and collects the collected traveling information and the collected peripheral image information. (S5 to S10), it is possible to determine traffic congestion conditions for each lane based on the above information (S5 to S10). By also using information based on captured images of vehicles with a wide possible range, it becomes possible to accurately determine traffic congestion conditions for each lane in a wider range.

It should be noted that the present invention is not limited to the above-described embodiments, and of course various improvements and modifications are possible without departing from the gist of the present invention.
For example, in the present embodiment, captured images captured by each vehicle 4 are collected as probe information collected from each vehicle 4, but image processing for the captured images is performed by each vehicle 4, and derived by image processing for the captured images. Other data (for example, distances to other vehicles, speeds of other vehicles, etc.) may be collected as probe information.

In addition, in the present embodiment, the position coordinates of each vehicle 4 are collected as probe information collected from each vehicle 4, but map matching with respect to the position coordinates is performed in each vehicle 4, and the vehicle 4 specified by the map matching travels. Lane may be collected as probe information.

Further, in the present embodiment, the traveling speed of other vehicles traveling in lanes other than the traveling lane and the density of other vehicles are calculated based on the information about the captured images collected from each vehicle 4, and the calculated traveling speed of the other vehicles is calculated. and the density of other vehicles (S6, S7). You can For example, the travel time of the link may be calculated.

Further, in the present embodiment, the server device 3 is the execution subject of the traffic congestion situation determination processing program shown in FIG. Also, the server device 3 and the navigation device 5 may be configured to execute a part thereof. When the navigation device 5 executes the traffic jam determination processing program, it is desirable to obtain travel information and captured images of other vehicles via the server device 3 or through inter-vehicle communication. Further, when the navigation device 5 executes a part of the traffic jam situation determination processing program, for example, the processing of S5 to S8 can be performed by the navigation device 5 of each vehicle.

Further, instead of the navigation device 5, the traffic condition determination system 1 can be configured by another device having a function of guiding traffic information and a function of performing route search processing using the traffic information. For example, a vehicle-mounted device other than the navigation device 5, a mobile phone, a smart phone, a tablet terminal, a personal computer, and the like are possible.

Moreover, although the embodiment embodying the traffic condition determination system according to the present invention has been described above, the traffic condition determination system can also have the following configuration, and in that case, the following effects can be obtained.

For example, the first configuration is as follows.
A traveling information collecting means (21) for collecting information specifying the traveling lane and vehicle speed of each vehicle (4) traveling on the road as traveling information, and an imaging device (7) provided for each vehicle traveling on the road. image information collecting means (21) for collecting, as peripheral image information, information relating to captured images of the surroundings of each vehicle; a traffic congestion determination means (21) for determining the traffic congestion condition of the road for each lane based on the peripheral image information.
According to the traffic condition determination system having the above-described configuration, when determining the traffic congestion condition of each lane based on the information collected from each vehicle traveling on the road, the reliability of the information is high, but the determination is possible. By combining driving information of vehicles with a narrow range and information based on captured images of vehicles with a wide range that can be determined, although the reliability of the information is low, traffic congestion conditions for each lane can be accurately determined over a wide range. can be determined.

A second configuration is as follows.
Based on the peripheral image information collected by the image information collection means (21), the traffic congestion determination means (21) determines the traffic in the lane around the imaging vehicle, which is the vehicle that captured the imaged image. The traffic flow is specified, and the traffic congestion condition of the road is determined for each lane based on the travel information collected by the travel information collecting means and the traffic flow of the specified lane.
According to the traffic situation determination system having the above configuration, it is possible to identify the traffic flow in the lane other than the lane in which the vehicle is traveling by using the captured image of the surroundings of the vehicle. Regarding lanes, it is possible to determine traffic congestion on roads with a relatively high degree of reliability.

A third configuration is as follows.
The congestion determining means (21) specifies the traveling speed of vehicles traveling in the lane and the density of the vehicles as the traffic flow in the lane.
According to the traffic situation determination system having the above configuration, it is possible to specify the traveling speed and the density of vehicles in lanes other than the lane in which the vehicle is traveling by using the captured image of the surroundings of the vehicle. It is possible to determine the traffic jam condition of the road with a relatively high degree of reliability even for lanes other than the lane in which the vehicle is traveling.

A fourth configuration is as follows.
The congestion determination means (21) prioritizes the determination result of the congestion determination based on the travel information when the congestion determination based on the travel information and the congestion determination based on the peripheral image information are possible for the same lane. used.
According to the traffic condition determination system having the above configuration, when determining the traffic congestion condition of each lane based on the information collected from each vehicle traveling on the road, different traffic congestion determination results are obtained for the same lane. Even in the case of overlap, it is possible to preferentially use the result of congestion determination with high reliability as information, so that it is possible to more accurately determine the congestion status of the road for each lane.

Also, the fifth configuration is as follows.
map information acquisition means (21) for acquiring map information (12) including information on the number of lanes of a road; determination result correction means (21) for correcting the determination result of the congestion determination means so as to match the number of lanes of the same road in the map information when the number of lanes does not match with the number of lanes of the same road.
According to the traffic condition determination system having the above configuration, when determining the traffic congestion condition of each lane based on the information collected from each vehicle traveling on the road, Even if the actual number of lanes does not match, it is possible to match the actual lane by correcting the determination result.

Also, the sixth configuration is as follows.
The traveling information collecting means (21) collects a history of coordinates indicating the current position of the vehicle (4) traveling on the road as information for specifying the traveling lane.
According to the traffic condition determination system having the above configuration, the traffic flow in the lane in which the vehicle travels can be specified by using the travel information of the vehicle. It is possible to determine the situation.

Moreover, the seventh configuration is as follows.
The traffic congestion determination means (21) determines the traffic congestion condition of the road for each lane based on the peripheral image information related to the imaged image captured by the imaged vehicle for each imaged vehicle that has imaged the imaged image. do.
According to the traffic condition determination system having the above configuration, it is possible to determine the traffic congestion condition of each lane by using information collected from each of a plurality of vehicles traveling on the road. Therefore, based on the information collected from each vehicle, it is possible to determine the road traffic congestion situation for each lane in a wider range.

Also, the eighth configuration is as follows.
The traffic congestion determination means (21) determines traffic congestion based on an imaged vehicle traveling in a lane closer to the lane to be determined when traffic congestion determination based on a plurality of imaged vehicles is possible for the same lane. Priority is given to judgment results.
According to the traffic condition determination system having the above configuration, when determining the traffic congestion condition of each lane based on the information collected from each vehicle traveling on the road, different traffic congestion determination results are obtained for the same lane. Even in the case of overlap, it is possible to preferentially use the result of congestion determination with high reliability as information, so that it is possible to more accurately determine the congestion status of the road for each lane.

Also, the ninth configuration is as follows.
The traffic congestion determination means (21) determines the traffic congestion determination result based on the imaging vehicle that entered late through the link including the lane to be determined, when the traffic congestion determination based on a plurality of imaging vehicles is possible for the same lane. are used with priority.
According to the traffic condition determination system having the above configuration, when determining the traffic congestion condition of each lane based on the information collected from each vehicle traveling on the road, different traffic congestion determination results are obtained for the same lane. Even in the case of overlap, it is possible to preferentially use the result of congestion determination with high reliability as information, so that it is possible to more accurately determine the congestion status of the road for each lane.

A tenth configuration is as follows.
The traffic congestion determination means (21) is capable of determining traffic congestion based on a plurality of imaging vehicles for the same lane, and when two or more different determination results are included, the number of determined imaging vehicles is larger. The result is used with priority over the determination result of congestion determination.
According to the traffic condition determination system having the above configuration, when determining the traffic congestion condition of each lane based on the information collected from each vehicle traveling on the road, different traffic congestion determination results are obtained for the same lane. Even in the case of overlap, it is possible to preferentially use the result of congestion determination with high reliability as information, so that it is possible to more accurately determine the congestion status of the road for each lane.

1 traffic situation determination system 2 probe center 3 server device 4 vehicle 5 navigation device 7 vehicle-mounted camera 11 server control ECU
12 Map information database
12 Probe information DB
13 Congestion information DB
21 CPUs
22 RAMs
23 ROMs
24 flash memory 65, 66, 67 imaging vehicle

Claims (10)

a traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road;
image information collecting means for collecting information about captured images captured around each vehicle by an imaging device provided in each vehicle running on the road as peripheral image information;
traffic congestion determination means for determining traffic congestion conditions of each lane based on the travel information collected by the travel information collection means and the peripheral image information collected by the image information collection means;
The congestion determination means preferentially uses the determination result of the congestion determination based on the travel information when the congestion determination based on the travel information and the congestion determination based on the peripheral image information are possible for the same lane. situation determination system. a traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road;
image information collecting means for collecting information about captured images captured around each vehicle by an imaging device provided in each vehicle running on the road as peripheral image information;
traffic congestion determination means for determining traffic congestion conditions of each lane based on the travel information collected by the travel information collection means and the peripheral image information collected by the image information collection means;
map information acquisition means for acquiring map information including information on the number of lanes on a road;
When the number of lanes of the road on which the congestion state is determined by the traffic congestion determination means does not match the number of lanes of the same road in the map information, the number of lanes of the same road is adjusted to match the number of lanes of the road in the map information. A traffic condition determination system, comprising a determination result correcting means for correcting the determination result. a traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road;
image information collecting means for collecting information about captured images captured around each vehicle by an imaging device provided in each vehicle running on the road as peripheral image information;
traffic congestion determination means for determining traffic congestion conditions of each lane based on the travel information collected by the travel information collection means and the peripheral image information collected by the image information collection means;
The congestion determination means is
When there are a plurality of image pickup vehicles that have picked up the image pickup vehicle, the traffic congestion condition of the road is determined for each lane based on the surrounding image information related to the image pickup image picked up by the image pickup vehicle. ,
If it is possible to determine congestion based on a plurality of imaged vehicles for the same lane, priority is given to the determination result of congestion determination based on imaged vehicles traveling in a lane closer to the lane to be determined. judgment system. a traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road;
image information collecting means for collecting information about captured images captured around each vehicle by an imaging device provided in each vehicle running on the road as peripheral image information;
traffic congestion determination means for determining traffic congestion conditions of each lane based on the travel information collected by the travel information collection means and the peripheral image information collected by the image information collection means;
The congestion determination means is
When there are a plurality of image pickup vehicles that have picked up the image pickup vehicle, the traffic congestion condition of the road is determined for each lane based on the surrounding image information related to the image pickup image picked up by the image pickup vehicle. ,
A traffic situation determination system that preferentially uses the result of congestion determination based on an imaging vehicle that has entered a link that includes a lane to be determined late, when it is possible to determine congestion based on a plurality of imaging vehicles for the same lane. . a traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road;
image information collecting means for collecting information about captured images captured around each vehicle by an imaging device provided in each vehicle running on the road as peripheral image information;
traffic congestion determination means for determining traffic congestion conditions of each lane based on the travel information collected by the travel information collection means and the peripheral image information collected by the image information collection means;
The congestion determination means is
When there are a plurality of image pickup vehicles that have picked up the image pickup vehicle, the traffic congestion condition of the road is determined for each lane based on the surrounding image information related to the image pickup image picked up by the image pickup vehicle. ,
Congestion determination based on a plurality of imaged vehicles is possible for the same lane, and when two or more different determination results are included, the determination result with a larger number of determined imaged vehicles is given priority over the determination result of congestion determination. Traffic situation judgment system used as The congestion determination means is
Based on the peripheral image information collected by the image information collecting means, identifying the traffic flow of the lane in the vicinity of the imaging vehicle, which is the vehicle that captured the captured image, and
6. The traffic according to any one of claims 1 to 5 , wherein a traffic congestion condition of each lane is determined based on the travel information collected by the travel information collecting means and the traffic flow of the specified lane. situation determination system. 7. The traffic condition determination system according to claim 6 , wherein said traffic congestion determination means specifies the traveling speed of vehicles traveling in the lane and the density of vehicles as the traffic flow of the lane. 8. The traffic condition determination system according to any one of claims 1 to 7 , wherein said traveling information collecting means collects a history of coordinates indicating current positions of vehicles traveling on the road as information for specifying a traveling lane. a traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road;
image information collecting means for collecting information about captured images captured around each vehicle by an imaging device provided in each vehicle running on the road as peripheral image information;
traffic congestion determination means for determining traffic congestion conditions of each lane based on the travel information collected by the travel information collection means and the peripheral image information collected by the image information collection means;
The congestion determination means preferentially uses the determination result of the congestion determination based on the travel information when the congestion determination based on the travel information and the congestion determination based on the peripheral image information are possible for the same lane. Situation determination device. a traveling information collecting means for collecting, as traveling information, information specifying the traveling lane and vehicle speed of each vehicle traveling on the road;
image information collecting means for collecting information about captured images captured around each vehicle by an imaging device provided in each vehicle running on the road as peripheral image information;
traffic congestion determination means for determining traffic congestion conditions of each lane based on the travel information collected by the travel information collection means and the peripheral image information collected by the image information collection means;
map information acquisition means for acquiring map information including information on the number of lanes on a road;
When the number of lanes of the road on which the congestion state is determined by the traffic congestion determination means does not match the number of lanes of the same road in the map information, the number of lanes of the same road is adjusted to match the number of lanes of the road in the map information. and a determination result correcting means for correcting the determination result.

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