Vehicle information prediction device, vehicle information prediction method, and vehicle information prediction program

Abstract

This vehicle information prediction device comprises: a route information acquisition unit for acquiring route information indicating a planned travel route of a target vehicle; an intersection information acquisition unit for acquiring intersection information pertaining to an intersection in the planned travel route indicated by the route information acquired by the route information acquisition unit; and a prediction unit for performing stop prediction processing that carries out, on the basis of the intersection information acquired by the intersection information acquisition unit, a prediction related to stoppage of the target vehicle at the intersection.

Description

Vehicle information prediction device, vehicle information prediction method, and vehicle information prediction program

[0001] This application claims priority from Japanese Patent Application No. 2024-69609, filed April 23, 2024, the disclosure of which is incorporated herein by reference in its entirety.

[0002] Patent Document 1 (JP 2011-75382 A) discloses the following technology: That is, a navigation device includes a current location detection means for detecting a current location, a remaining energy detection means for detecting a remaining amount of drive energy of the host vehicle, a storage means for storing map information including location information of drive energy replenishment locations for the host vehicle, an energy consumption calculation means for calculating, based on the map information, an amount of drive energy to be consumed when traveling along any link included in the map information, a route calculation means for calculating a route from the current location to a destination, and a route guidance means for guiding the host vehicle along the route based on a result of calculation by the route calculation means, and the route calculation means calculates a recommended route with the lowest cost from among routes in which the remaining amount does not fall below a predetermined threshold, based on the map information, the remaining amount at the time of departure detected by the remaining energy detection means, and the consumption amount calculated by the energy consumption calculation means.

[0003] JP 2011-75382 A

[0004] The vehicle information prediction device disclosed herein includes a route information acquisition unit that acquires route information indicating a planned driving route of a target vehicle, an intersection information acquisition unit that acquires intersection information regarding intersections on the planned driving route indicated by the route information acquisition unit, and a prediction unit that performs a stop prediction process that predicts whether the target vehicle will stop at the intersection based on the intersection information acquired by the intersection information acquisition unit.

[0005] One aspect of the present disclosure can be realized not only as a vehicle information prediction device equipped with such a characteristic processing unit, but also as a semiconductor integrated circuit that realizes part or all of the vehicle information prediction device, or as a system that includes the vehicle information prediction device.

[0006] FIG. 1 is a diagram illustrating an example of a configuration of a vehicle information prediction system according to an embodiment of the present disclosure. FIG. 2 is a diagram illustrating an example of a configuration of an in-vehicle device according to an embodiment of the present disclosure. FIG. 3 is a diagram illustrating an example of a configuration of a vehicle information prediction device according to an embodiment of the present disclosure. FIG. 4 is a diagram illustrating an example of an intersection table stored by the vehicle information prediction device according to an embodiment of the present disclosure. FIG. 5 is a diagram illustrating an example of intersections on a planned driving route indicated by route information acquired by the vehicle information prediction device according to an embodiment of the present disclosure. FIG. 6 is a diagram illustrating an example of a congestion length table stored by the vehicle information prediction device according to an embodiment of the present disclosure. FIG. 7 is a diagram illustrating an example of a traffic light-related table stored by the vehicle information prediction device according to an embodiment of the present disclosure. FIG. 8 is a diagram for explaining an example of a stop prediction process performed by the vehicle information prediction device according to an embodiment of the present disclosure. FIG. 9 is a diagram illustrating an example of a gradient table stored by the vehicle information prediction device according to an embodiment of the present disclosure. FIG. 10 is a diagram illustrating an example of time-series data of the speed of a target vehicle created in a creation process performed by the vehicle information prediction device according to an embodiment of the present disclosure. FIG. 11 is a diagram illustrating an example of a specifications table stored by the vehicle information prediction device according to an embodiment of the present disclosure. 12 and 13 are flowcharts illustrating an example of an operation procedure when a vehicle information prediction device according to an embodiment of the present disclosure performs a stop prediction process.

[0007] 2. Description of the Related Art Conventionally, when a vehicle travels from a departure point to a destination, a technique has been developed that calculates a recommended route according to the remaining amount of energy for driving the vehicle.

[0008] [Problem to be Solved by the Present Disclosure] When a vehicle travels according to a planned travel route proposed by, for example, a navigation device, there is a possibility that the arrival time at the destination may be delayed or the vehicle may consume a lot of energy due to traffic volume at intersections, signal control at intersections, etc. There is a need for a technology that can grasp the influence of intersections when a target vehicle travels along a planned travel route.

[0009] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to provide a vehicle information prediction device, a vehicle information prediction method, and a vehicle information prediction program that are capable of grasping the influence of intersections when a target vehicle is traveling along a planned route.

[0010] Effect of the Present Disclosure According to the present disclosure, it is possible to grasp the influence of an intersection when a target vehicle travels along a planned travel route.

[0011] [Description of Embodiments of the Present Disclosure] First, the contents of embodiments of the present disclosure will be listed and described. (1) A vehicle information prediction device according to an embodiment of the present disclosure includes a route information acquisition unit that acquires route information indicating a planned driving route of a target vehicle, an intersection information acquisition unit that acquires intersection information regarding intersections on the planned driving route indicated by the route information acquisition unit, and a prediction unit that performs stop prediction processing to predict whether the target vehicle will stop at the intersection based on the intersection information acquired by the intersection information acquisition unit.

[0012] With this configuration, it is possible to collect route information indicating the planned driving route of the vehicle, and to predict whether the target vehicle will stop at the intersection using intersection information on the planned driving route, thereby understanding the influence of the intersection on the target vehicle when traveling along the planned driving route.

[0013] (2) In the above (1), the prediction unit may predict whether the target vehicle will stop at the intersection in the stop prediction process.

[0014] With this configuration, when the target vehicle travels along the planned travel route, it can be determined whether the target vehicle will stop at an intersection or pass through the intersection without stopping.

[0015] (3) In the above (1) or (2), the prediction unit may predict a stopping time of the target vehicle at the intersection in the stopping prediction process.

[0016] With this configuration, when the target vehicle is traveling along the planned travel route, it is possible to know the planned stopping time of the target vehicle at an intersection.

[0017] (4) In any of (1) to (3) above, the prediction unit may, in the stop prediction process, predict the intersection at which the target vehicle will stop, among the multiple intersections on the planned driving route.

[0018] With this configuration, when the target vehicle travels along the planned travel route, it is possible to grasp the intersections at which the target vehicle is scheduled to stop.

[0019] (5) In any of (1) to (4) above, the prediction unit may further predict a congestion passing time, which is the time required for the target vehicle to pass through the congestion at the intersection, based on the intersection information acquired by the intersection information acquisition unit, and the prediction unit may perform the stop prediction process using the predicted congestion passing time.

[0020] With this configuration, for example, if there is a possibility of traffic congestion at an intersection during the time period when the target vehicle is traveling along its planned route, the stop prediction process can be performed taking into account the impact of the traffic congestion, thereby improving prediction accuracy.

[0021] (6) In any of (1) to (5) above, the prediction unit may further predict, based on the intersection information acquired by the intersection information acquisition unit, an allowable passing time, which is the time given to the target vehicle by a traffic light installed at the intersection for the target vehicle to pass through the intersection, and the prediction unit may perform the stop prediction process using the predicted allowable passing time.

[0022] With this configuration, for example, the stop prediction process can be performed taking into account the length of time that the target vehicle has the right of way at an intersection, thereby improving prediction accuracy.

[0023] (7) In any of (1) to (6) above, the vehicle information prediction device may further include a creation unit that performs a creation process to create time series data of the speed of the target vehicle when the target vehicle travels along the planned travel route, based on the prediction result of the stop prediction process by the prediction unit.

[0024] With this configuration, it is possible to create time series data of vehicle speed taking into account the prediction results of the stop prediction process, thereby making it possible to understand the effect that intersections on the planned driving route have on the vehicle speed of the target vehicle.

[0025] (8) In the above (7), the vehicle information prediction device may further include a gradient information acquisition unit that acquires gradient information indicating the gradient of the planned driving route indicated by the route information acquired by the advance route information acquisition unit, and the creation unit may perform the creation process further based on the gradient information acquired by the gradient information acquisition unit.

[0026] With this configuration, it is possible to grasp the influence that the gradient of the road on which the target vehicle is scheduled to travel, in addition to the intersection, has on the vehicle speed of the target vehicle.

[0027] (9) In any of (1) to (8) above, the vehicle information prediction device may further include a calculation unit that calculates the energy consumption of the target vehicle when the target vehicle travels along the planned driving route based on the prediction result of the stop prediction process by the prediction unit.

[0028] With this configuration, the energy consumption of the target vehicle can be calculated taking into account the prediction results of the stop prediction process, thereby improving the accuracy of the calculation results.

[0029] (10) A vehicle information prediction method according to an embodiment of the present disclosure is a vehicle information prediction method in a vehicle information prediction device, and includes the steps of acquiring route information indicating a planned driving route of a target vehicle, acquiring intersection information regarding intersections on the planned driving route indicated by the acquired route information, and performing a stop prediction process that predicts whether the target vehicle will stop at the intersection based on the acquired intersection information.

[0030] This method collects route information indicating the planned driving route of the vehicle, and uses intersection information about intersections on the planned driving route to predict whether the target vehicle will stop at the intersection. Therefore, it is possible to understand the influence of intersections on the target vehicle when traveling along the planned driving route.

[0031] (11) A vehicle information prediction program according to an embodiment of the present disclosure is a vehicle information prediction program used in a vehicle information prediction device, and is a program for causing a computer to function as a route information acquisition unit that acquires route information indicating a planned driving route of a target vehicle, an intersection information acquisition unit that acquires intersection information regarding intersections on the planned driving route indicated by the route information acquisition unit, and a prediction unit that performs stop prediction processing to predict whether the target vehicle will stop at the intersection based on the intersection information acquired by the intersection information acquisition unit.

[0032] With this configuration, it is possible to collect route information indicating the planned driving route of the vehicle, and to predict whether the target vehicle will stop at the intersection using intersection information on the planned driving route, thereby understanding the influence of the intersection on the target vehicle when traveling along the planned driving route.

[0033] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the drawings, identical or corresponding parts are designated by the same reference numerals, and their description will not be repeated. Furthermore, at least some of the embodiments described below may be combined in any manner.

[0034] FIG. 1 is a diagram illustrating an example of the configuration of a vehicle information prediction system according to an embodiment of the present disclosure. Referring to FIG. 1, a vehicle information prediction system 501 includes a vehicle information prediction device 101 and one or more on-board devices 201. The vehicle information prediction device 101 and each on-board device 201 transmit and receive information via an external network 151 such as the Internet. The on-board device 201 is mounted on a vehicle 1. The vehicle 1 is, for example, an electric vehicle. Note that the vehicle 1 is not limited to an electric vehicle, and may be a hybrid vehicle, a gasoline vehicle, or the like.

[0035] The in-vehicle device 201 transmits route information indicating the planned driving route of the vehicle 1 to the vehicle information prediction device 101 via the external network 151 .

[0036] The vehicle information prediction device 101 receives route information from the in-vehicle device 201 via the external network 151, and predicts whether the vehicle 1 will stop at an intersection CS on the planned driving route indicated by the received route information.

[0037] The vehicle information prediction device 101 is used, for example, by a business operator or an individual who manages the operation of the vehicle 1. The vehicle information prediction device 101 is, for example, a server. Note that the vehicle information prediction device 101 is not limited to a server, and may also be a navigation device or the like installed in the vehicle 1.

[0038] [In-Vehicle Device] Fig. 2 is a diagram illustrating an example of the configuration of an in-vehicle device according to an embodiment of the present disclosure. Referring to Fig. 2, the in-vehicle device 201 includes an in-vehicle communication unit 11, a calculation unit 12, an exterior communication unit 13, and a storage unit 14. The in-vehicle communication unit 11, the calculation unit 12, and the exterior communication unit 13 are partly or entirely realized by a processing circuit including one or more processors. The storage unit 14 is, for example, a non-volatile memory included in the processing circuit.

[0039] For example, the in-vehicle device 201 is connected to in-vehicle devices 52A and 52B, which are a plurality of in-vehicle devices 52, via a communication bus 61. Specifically, the communication bus 61 is, for example, a CAN (Controller Area Network) bus that complies with the CAN standard.

[0040] 2, the in-vehicle devices 52A and 52B are a navigation device and a speed sensor, respectively. Hereinafter, the in-vehicle devices 52A and 52B are also referred to as the navigation device 52A and the speed sensor 52B, respectively.

[0041] (Route Information) The navigation device 52A accepts, for example, input of the departure point, destination, and departure time ta of the vehicle 1 by the user of the vehicle 1. Upon accepting the input of the departure point, destination, and departure time ta, the navigation device 52A creates route information indicating a planned driving route from the departure point to the destination, the departure time ta, a planned passage time tb which is the planned time at which the vehicle will pass an intersection CS located between the departure point and the destination, and a planned arrival time tc which is the planned time at which the vehicle will arrive at the destination.

[0042] For example, the planned driving route includes a position coordinate G1 of a departure point, a position coordinate G2 of a destination point, and a position coordinate G3 of one or more intersections CS. Each of the position coordinate G1, the position coordinate G2, and the position coordinate G3 is expressed, for example, by latitude and longitude. An example will be described below in which the planned driving route includes the position coordinate G1, the position coordinate G2, and multiple position coordinates G3.

[0043] After creating the route information, the navigation device 52A transmits the route information to the in-vehicle device 201 .

[0044] In the in-vehicle device 201, the in-vehicle communication unit 11 receives route information from the navigation device 52A. Then, the in-vehicle communication unit 11 outputs the received route information to the out-vehicle communication unit 13.

[0045] For example, the storage unit 14 stores vehicle identification information (hereinafter also referred to as a “vehicle ID (Identifier)”) for identifying the vehicle 1. The vehicle ID is an ID unique to each vehicle 1.

[0046] When the exterior-vehicle communication unit 13 receives the route information from the interior-vehicle communication unit 11, it creates an IP packet P1 that includes the vehicle ID stored in the storage unit 14 in the route information and that includes, as a source IP address and a destination IP address, the IP address of the on-vehicle device 201 and the IP address of the vehicle information prediction device 101, both of which are stored in the storage unit 14. The exterior-vehicle communication unit 13 then transmits the created IP packet P1 to the vehicle information prediction device 101 via the wireless base station device and the external network 151. Hereinafter, the vehicle 1 equipped with the on-vehicle device 201 that is the sender of the route information will also be referred to as the target vehicle 1A.

[0047] (Probe Data) Furthermore, for example, the navigation device 52A generates probe data indicating the position of the vehicle 1 while the vehicle 1 is traveling and the time tp at which the vehicle 1 passes that position. The position of the vehicle 1 is, for example, a coordinate based on radio waves transmitted from a GPS (Global Positioning System) satellite.

[0048] When the navigation device 52A creates the probe data, it transmits the created probe data to the in-vehicle device 201. The navigation device 52A creates and transmits the probe data, for example, periodically.

[0049] In the in-vehicle device 201, when the in-vehicle communication unit 11 receives the probe data from the navigation device 52A, the in-vehicle communication unit 11 stores the received probe data in the storage unit 14.

[0050] (Speed ​​Information) The speed sensor 52B measures the traveling speed of the vehicle 1 while the vehicle 1 is traveling, and transmits speed information indicating the measurement result and the measurement time ts to the in-vehicle device 201. The speed sensor 52B measures the traveling speed and transmits the speed information, for example, periodically.

[0051] In the in-vehicle device 201, when the in-vehicle communication unit 11 receives the speed information from the speed sensor 52B, the in-vehicle communication unit 11 stores the received speed information in the storage unit 14.

[0052] (Average Speed ​​Information) The calculation unit 12 performs a calculation process to calculate an average traveling speed Va of the vehicle 1 while the vehicle 1 is traveling. More specifically, for example, when a processing timing K of the calculation process arrives, the calculation unit 12 calculates the average traveling speed Va of the vehicle 1 based on a plurality of pieces of speed information stored in the storage unit 14.

[0053] Specifically, for example, when processing timing K of the calculation process arrives, the calculation unit 12 acquires from the storage unit 14 a plurality of pieces of speed information accumulated during a period A from the previous processing timing K to the current processing timing K. Then, the calculation unit 12 calculates the average traveling speed Va of the vehicle 1 during the period A based on the acquired plurality of pieces of speed information. The length of the period A is, for example, 10 minutes.

[0054] After calculating the average traveling speed Va, the calculation unit 12 selects one or more pieces of probe data (hereinafter also referred to as "corresponding probe data") that indicate a time tp included in the period A corresponding to the average traveling speed Va from the plurality of probe data stored in the memory unit 14.

[0055] When the calculation unit 12 selects the corresponding probe data, it outputs to the outside-vehicle communication unit 13 average speed information C indicating the position of the vehicle 1 indicated by the selected corresponding probe data, the calculated average traveling speed Va, and the period A corresponding to the average traveling speed Va.

[0056] When the exterior-vehicle communication unit 13 receives the average speed information C from the calculation unit 12, it creates an IP packet P2 that includes the vehicle ID stored in the storage unit 14 in the average speed information C and that includes, as a source IP address and a destination IP address, the IP address of the in-vehicle device 201 and the IP address of the vehicle information prediction device 101. The exterior-vehicle communication unit 13 then transmits the created IP packet P2 to the vehicle information prediction device 101 via the wireless base station device and the external network 151.

[0057] The in-vehicle device 201 and each in-vehicle device 52 may be configured to perform communication in accordance with standards other than CAN, such as CAN FD (CAN with Flexible Data Rate), Ethernet (registered trademark), FlexRay (registered trademark), MOST (Media Oriented System Transport) (registered trademark), LIN (Local Interconnect Network), and CXPI (Clock Extension Peripheral Interface) (registered trademark). The in-vehicle device 201 and each in-vehicle device 52 may also be configured to perform wireless communication in accordance with standards such as Bluetooth.

[0058] [Vehicle Information Prediction Device] Fig. 3 is a diagram illustrating an example of the configuration of a vehicle information prediction device according to an embodiment of the present disclosure. Referring to Fig. 3, the vehicle information prediction device 101 includes a vehicle information acquisition unit 21, an intersection information acquisition unit 22, a prediction unit 23, a creation unit 24, a gradient information acquisition unit 25, a calculation unit 26, a notification unit 27, and a storage unit 28. Some or all of the vehicle information acquisition unit 21, the intersection information acquisition unit 22, the prediction unit 23, the creation unit 24, the gradient information acquisition unit 25, the calculation unit 26, and the notification unit 27 are realized, for example, by a processing circuit including one or more processors. The storage unit 28 is, for example, a non-volatile memory included in the processing circuit. The vehicle information acquisition unit 21 is an example of a route information acquisition unit.

[0059] (Vehicle Information Acquisition Unit) For example, the vehicle information acquisition unit 21 acquires route information and average speed information C. Specifically, for example, when the vehicle information acquisition unit 21 receives an IP packet P1 including route information from the in-vehicle device 201 via the wireless base station device and the external network 151, the vehicle information acquisition unit 21 outputs the route information included in the IP packet P1 to the intersection information acquisition unit 22, the gradient information acquisition unit 25, and the calculation unit 26.

[0060] Also, for example, when the vehicle information acquisition unit 21 receives an IP packet P2 containing average speed information C from the in-vehicle device 201 via the wireless base station device and the external network 151, it stores the average speed information C contained in the IP packet P2 in the memory unit 28.

[0061] (Intersection Table) FIG. 4 is a diagram illustrating an example of an intersection table stored by the vehicle information prediction device according to the embodiment of the present disclosure.

[0062] 4, for example, storage unit 28 stores an intersection table Tb1 indicating the correspondence between the position coordinates of intersections CS (hereinafter also referred to as "intersection coordinates") and identification information for identifying the intersections CS (hereinafter also referred to as "intersection IDs"). In intersection table Tb1, the intersection coordinates are coordinates represented by, for example, latitude and longitude.

[0063] In the intersection table Tb1 shown in Fig. 4, the intersection ID corresponding to the intersection coordinates (X1, Y1) is AAA, the intersection ID corresponding to the intersection coordinates (X2, Y2) is BBB, and the intersection ID corresponding to the intersection coordinates (X3, Y3) is CCC.

[0064] Hereinafter, the intersection CS with intersection ID "AAA", the intersection CS with intersection ID "BBB", and the intersection CS with intersection ID "CCC" will also be referred to as intersection CS1, intersection CS2, and intersection CS3, respectively.

[0065] FIG. 5 is a diagram illustrating an example of intersections on a planned driving route indicated by route information acquired by a vehicle information prediction device according to an embodiment of the present disclosure.

[0066] 5 , at an intersection CS, inlet roads 31A, 31B, 31C, and 31D, which are inlet roads 31, are connected to outlet roads 41A, 41B, 41C, and 41D, which are outlet roads 41. At the intersection CS, a traffic light 51 is provided for each inlet road 31.

[0067] Hereinafter, the node connected to intersection CS via inlet road 31A and outlet road 41A, the node connected to intersection CS via inlet road 31B and outlet road 41B, the node connected to intersection CS via inlet road 31C and outlet road 41C, and the node connected to intersection CS via inlet road 31D and outlet road 41D will also be referred to as node N1, node N2, node N3, and node N4, respectively.

[0068] (Intersection information acquisition unit) Referring again to Figure 3, the intersection information acquisition unit 22 acquires intersection information regarding an intersection CS (hereinafter also referred to as a "target intersection") on the planned driving route indicated by the route information acquired by the vehicle information acquisition unit 21.

[0069] More specifically, for example, the intersection information acquisition unit 22 acquires, as intersection information, congestion length information indicating congestion length L at the target intersection and green time information indicating green time Tg at the target intersection. For example, congestion length L is the congestion length from stop line S on the approach road 31 to the target intersection, as shown in Fig. 5. Green time Tg is the time during which vehicle 1 has the right of way at the target intersection.

[0070] For example, when receiving route information from the vehicle information acquisition unit 21, the intersection information acquisition unit 22 refers to the intersection table Tb1 in the storage unit 28 to confirm, for each position coordinate G3 included in the planned travel route indicated by the route information, the intersection coordinates that are the same as the position coordinate G3. Then, the intersection information acquisition unit 22 identifies the intersection ID corresponding to the confirmed intersection coordinates.

[0071] For example, the storage unit 28 stores map information for an area including the planned driving route indicated by the route information received from the vehicle information acquisition unit 21. When the intersection information acquisition unit 22 identifies a plurality of intersection IDs respectively corresponding to a plurality of position coordinates G3 included in the planned driving route, the intersection information acquisition unit 22 uses the map information in the storage unit 28 and the planned driving route to confirm the traveling direction of the target vehicle 1A at each target intersection of the identified intersection IDs.

[0072] For example, the storage unit 28 stores a node table indicating the correspondence between an intersection ID, a traveling direction of the vehicle 1, an incoming node connected to the intersection CS, and an outgoing node connected to the intersection CS.

[0073] The intersection information acquisition unit 22 uses the node table in the storage unit 28 to check the incoming node and the outgoing node at each target intersection.

[0074] Specifically, for example, the intersection information acquisition unit 22 confirms the direction of travel of the target vehicle 1A at each target intersection, and then, by referring to the node table in the memory unit 28, confirms the intersection ID of the target intersection and the incoming node and outgoing node corresponding to the direction of travel.

[0075] (Traffic Jam Length Table) FIG. 6 is a diagram illustrating an example of a traffic jam length table stored by the vehicle information prediction device according to the embodiment of the present disclosure.

[0076] Referring to FIG. 6, the storage unit 28 stores a congestion length table Tb2 indicating the correspondence between a time period Ts, an intersection ID, an incoming node, an outgoing node, and a congestion length L.

[0077] In the congestion length table Tb2 shown in FIG. 6 , the congestion length L is "300 m" when a vehicle 1 enters an intersection CS with an intersection ID "AAA" from an entering node "N1" during a time period Ts from 2:00 PM to 3:00 PM and exits from the intersection CS to an exit node "N2" from the entering node "N4" during a time period Ts from 6:00 PM to 7:00 PM, and the congestion length L is "500 m" when a vehicle 1 enters an intersection CS with an intersection ID "AAA" from an entering node "N4" during a time period Ts from 6:00 PM to 7:00 PM, and exits from the intersection CS to an exit node "N2". The congestion length L is "100 m" when a vehicle 1 enters an intersection CS with an intersection ID "BBB" from an entering node "N4" during a time period Ts from 6:00 PM to 7:00 PM, and exits from an exit node "N2". During the time period Ts from 6:00 PM to 7:00 PM, when vehicle 1 enters intersection CS with intersection ID "CCC" from entering node "N4" and exits from exit node "N2", the congestion length L is "80 m".

[0078] 3 , once the intersection information acquisition unit 22 has confirmed the entering node and the exit node at each target intersection, it reads out the congestion length table Tb2 from the storage unit 28. Then, by referring to the congestion length table Tb2, the intersection information acquisition unit 22 identifies, for each target intersection, the intersection ID of the target intersection, the time period Ts including the scheduled time tb of passing through the target intersection indicated by the route information received from the vehicle information acquisition unit 21, and the congestion length L corresponding to the confirmed entering node and the confirmed exit node.

[0079] When the intersection information acquisition unit 22 identifies the congestion length L for each target intersection, it outputs to the prediction unit 23 congestion length information indicating the intersection coordinates of the target intersection, the identified congestion length L, the time period Ts corresponding to the congestion length L, and the vehicle ID included in the route information received from the vehicle information acquisition unit 21.

[0080] (Signal-Related Table) FIG. 7 is a diagram illustrating an example of a signal-related table stored by the vehicle information prediction device according to the embodiment of the present disclosure.

[0081] Referring to Figure 7, the memory unit 28 stores a signal relationship table Tb3 that shows the correspondence between the time period Ts, the intersection ID, the entry node, the exit node, the cycle length of the traffic light 51 installed at the intersection CS, and the split of the traffic light 51.

[0082] The "cycle length" in the signal-related table Tb3 refers to the time required for one cycle of the signal display of the traffic light 51. In other words, the cycle length of the traffic light 51 is the time from the start time of the green light of the traffic light 51 to the start time of the next green light. Note that the cycle length of the traffic light 51 may also be the time from the start time of the red light of the traffic light 51 to the start time of the next red light.

[0083] The "split" in the signal-related table Tb3 is the time ratio of each aspect in one signal cycle. Specifically, for example, the split is the ratio of the length of the green time Tg allocated to each aspect to the cycle length.

[0084] In the signal-related table Tb3 shown in FIG. 7 , the cycle length and split of the traffic light 51 are "60 seconds" and "0.7," respectively, when the vehicle 1 enters the intersection CS with the intersection ID "AAA" from the entering node "N1" and exits from the exit node "N2" during the time period Ts from 2:00 PM to 3:00 PM. The cycle length and split of the traffic light 51 are "62.5 seconds" and "0.8," respectively, when the vehicle 1 enters the intersection CS with the intersection ID "AAA" from the entering node "N4" and exits from the exit node "N2" during the time period Ts from 6:00 PM to 7:00 PM. The cycle length and split of the traffic light 51 are "75 seconds" and "0.8," respectively, when the vehicle 1 enters the intersection CS with the intersection ID "BBB" from the entering node "N4" and exits from the exit node "N2" during the time period Ts from 6:00 PM to 7:00 PM. During the time period Ts from 6:00 p.m. to 7:00 p.m., when vehicle 1 enters intersection CS with intersection ID "CCC" from entering node "N4" and exits from exit node "N2," the cycle length and split of traffic light 51 are "50 seconds" and "0.8," respectively.

[0085] 3 again, after confirming the entering node and the exiting node at each target intersection, the intersection information acquisition unit 22 reads the signal-related table Tb3 from the storage unit 28. Then, by referring to the signal-related table Tb3, the intersection information acquisition unit 22 identifies, for each target intersection, the intersection ID of the target intersection, the time period Ts including the scheduled time tb of passing through the target intersection indicated in the route information from the vehicle information acquisition unit 21, and the cycle length and split corresponding to the confirmed entering node and the confirmed exiting node.

[0086] The intersection information acquisition unit 22 uses the signal-related table Tb3 to identify the cycle length and split for each target intersection, and then calculates the green time Tg at the target intersection by multiplying the identified cycle length and split. The intersection information acquisition unit 22 then outputs to the prediction unit 23 the green time information indicating the calculated green time Tg, the intersection ID and time period Ts corresponding to the green time Tg, and the vehicle ID included in the route information received from the vehicle information acquisition unit 21.

[0087] (Prediction Unit) The prediction unit 23 performs a stop prediction process to predict whether the target vehicle 1A will stop at the intersection CS based on the congestion length information and green time information acquired by the intersection information acquisition unit 22.

[0088] <Traffic jam passing time> More specifically, for example, the prediction unit 23 performs a time prediction process B1 to predict the time required for the target vehicle 1A to pass through the traffic jam at the target intersection (hereinafter also referred to as the "traffic jam passing time T1") based on the traffic jam length information acquired by the intersection information acquisition unit 22 and the average speed information C acquired by the vehicle information acquisition unit 21.

[0089] Specifically, for example, when the prediction unit 23 receives congestion length information from the intersection information acquisition unit 22, it acquires, from the storage unit 28, a plurality of pieces of average speed information C that include the same vehicle ID as the vehicle ID indicated in the congestion length information. Then, from the acquired plurality of pieces of average speed information C, the prediction unit 23 selects average speed information Ca that indicates the average traveling speed Va in the time period Ts indicated by the congestion length information and that is the average traveling speed Va at a position closest to the intersection coordinates indicated by the congestion length information.

[0090] Then, the traffic jam passing time T1 is calculated using the traffic jam length L indicated by the traffic jam length information received from the intersection information acquisition unit 22 and the average traveling speed Va indicated by the selected average speed information Ca.

[0091] For example, the relationship between the traffic jam passing time T1, the traffic jam length L, and the average traveling speed Va is expressed by the following equation (1): T1=L / Va (1)

[0092] <Allowed passing time> Furthermore, for example, the prediction unit 23 performs a time prediction process B2 to predict the time (hereinafter also referred to as the "allowed passing time T2") given to the target vehicle 1A by the traffic light 51 installed at the target intersection for the target vehicle 1A to pass through the target intersection, based on the green time information acquired by the intersection information acquisition unit 22.

[0093] More specifically, for example, when the prediction unit 23 receives green time information from the intersection information acquisition unit 22, when performing the first time prediction process B2 for the target vehicle 1A having the vehicle ID indicated in the green time information, the prediction unit 23 predicts the green time Tg indicated by the green time information as the permissible passage time T2. An example of the prediction unit 23 performing the second or subsequent time prediction process B2 for the target vehicle 1A having the vehicle ID indicated in the green time information received from the intersection information acquisition unit 22 will be described later.

[0094] For example, the prediction unit 23 performs a stop prediction process using the predicted traffic jam passing time T1 and permissible passing time T2. The stop prediction process includes a first prediction, a second prediction, and a third prediction.

[0095] <First Prediction> For example, in the stop prediction process, the prediction unit 23 performs a first prediction to predict whether the target vehicle 1A will stop at the target intersection.

[0096] More specifically, for example, the prediction unit 23 calculates an evaluation value H, which is a criterion for the stop prediction process, using the predicted traffic jam passing time T1 and permissible passing time T2 according to the following formula (3): In formula (3), a is a coefficient: H=T1+a×T2 (3)

[0097] For example, if the calculated evaluation value H is greater than zero, the prediction unit 23 determines that the target vehicle 1A will stop at the target intersection.

[0098] On the other hand, if the calculated evaluation value H is equal to or less than zero, the prediction unit 23 determines that the target vehicle 1A will pass through the target intersection without stopping.

[0099] For example, when the prediction unit 23 predicts the traffic jam passing time T1 and the permissible passing time T2, it calculates a coefficient a based on the predicted traffic jam passing time T1 and the permissible passing time T2 according to a predetermined calculation formula N.

[0100] For example, in the stop prediction process for a target intersection that is significantly affected by the congestion length L, the coefficient a is set to a negative value according to the calculation formula N. Specifically, the target intersection is an intersection CS or the like where the congestion passing time T1 is equal to or greater than a predetermined threshold. In this case, since there is a high possibility that the target vehicle 1A will stop at the target intersection, the coefficient a is set to, for example, a negative value.

[0101] On the other hand, in the stop prediction process for a target intersection that is heavily influenced by signal control by a signal control device that controls traffic light 51 shown in Fig. 5, coefficient a is set to a positive value according to arithmetic formula N. Specifically, the target intersection is an intersection CS that includes a minor road whose width is narrower than that of the main road, an intersection CS where the green time Tg changes depending on the traffic volume, etc.

[0102] Generally, at an intersection CS that includes a minor road whose width is smaller than that of the main road, the green time Tg on the minor road is shorter than the green time Tg on the main road. In other words, when the target vehicle 1A travels on a minor road of the intersection CS, there is a high possibility that the target vehicle 1A will stop at the intersection CS. Therefore, even if a traffic jam has not occurred at the target intersection or the traffic jam length L is short, if the target intersection includes a minor road, the coefficient a is set to, for example, a positive value so that a prediction result that the target vehicle 1A will stop can be obtained.

[0103] 8 is a diagram illustrating an example of a stop prediction process performed by the vehicle information prediction device 101 according to an embodiment of the present disclosure. Fig. 8 illustrates the stop prediction process performed by the vehicle information prediction device 101 when target intersections are intersections CS1, CS2, and CS3, and a target vehicle 1A passes through the intersections CS1, CS2, and CS3 in this order during a time period Ts from 6:00 PM to 7:00 PM.

[0104] (a) Intersection CS1 With reference to FIGS. 3 and 8, in the vehicle information prediction device 101, first, the prediction unit 23 predicts whether or not the target vehicle 1A will stop at the intersection CS1.

[0105] More specifically, for example, by referring to the congestion length table Tb2 shown in Figure 6, the prediction unit 23 confirms that the congestion length L when the target vehicle 1A enters the intersection CS1 from the entry node N4, i.e., the entry road 31D, during the time period Ts from 6:00 p.m. to 7:00 p.m., and exits from the intersection CS1 to the exit node N2, i.e., the exit road 41B, is "500 m."

[0106] The prediction unit 23 also acquires, from the storage unit 28, average speed information C indicating the average traveling speed Va (hereinafter also referred to as "average traveling speed Va1") when the target vehicle 1A passes through the position closest to the intersection CS1 during the time slot Ts from 6:00 PM to 7:00 PM. In the example shown in FIG. 8, the average traveling speed Va1 is assumed to be 50 m / s.

[0107] After confirming the congestion length L and the average traveling speed Va1, the prediction unit 23 predicts the congestion passing time T1 using the confirmed congestion length L and average traveling speed Va1. Specifically, for example, the prediction unit 23 calculates the congestion passing time T1 at the intersection CS1 by substituting the confirmed congestion length L and average traveling speed Va1 into equation (1). In the example shown in FIG. 8 , the congestion passing time T1 at the intersection CS1 is 10 seconds.

[0108] In addition, by referring to the signal-related table Tb3 shown in Figure 7, the prediction unit 23 confirms that the cycle length and split when the target vehicle 1A flows into the intersection CS1 from the entrance road 31D and exits to the exit road 41B during the time period Ts from 6:00 p.m. to 7:00 p.m. are "62.5 seconds" and "0.8", respectively.

[0109] The prediction unit 23 then calculates the green time Tg at the intersection CS1 by multiplying the confirmed cycle length and split. In the example shown in Fig. 8, the green time Tg at the intersection CS1 is 50 seconds. The prediction unit 23 then predicts the permissible passage time T2 using the calculated green time Tg.

[0110] 8, the intersection CS1 is the first intersection CS that is the target of the stop prediction process, and the prediction unit 23 performs the first time prediction process B2 for the target vehicle 1A. In this case, the prediction unit 23 predicts the calculated green time Tg of "50 seconds" as the allowed passage time T2.

[0111] After predicting the traffic jam passing time T1 and the permissible passing time T2 at the intersection CS1, the prediction unit 23 calculates the coefficient a in the formula (3) using the predicted traffic jam passing time T1 and permissible passing time T2 according to the calculation formula N. Here, the coefficient a is assumed to be −0.1.

[0112] When the traffic jam passing time T1 at intersection CS1, the allowable passing time T2 at intersection CS1, and the coefficient a are "10 seconds," "50 seconds," and "-0.1," respectively, the evaluation value H calculated using equation (3) is 5 seconds. In this case, the prediction unit 23 determines that the target vehicle 1A will stop at intersection CS1 because the calculated evaluation value H is greater than zero.

[0113] (b) Intersection CS2 Next, the prediction unit 23 predicts whether the target vehicle 1A will stop at intersection CS2.

[0114] More specifically, for example, by referring to the congestion length table Tb2 shown in Figure 6, the prediction unit 23 confirms that the congestion length L when the target vehicle 1A flows into the intersection CS2 from the entrance road 31D and then flows out to the exit road 41B during the time period Ts from 18:00 to 19:00 is "100 m".

[0115] The prediction unit 23 also acquires, from the storage unit 28, average speed information C indicating the average traveling speed Va (hereinafter also referred to as "average traveling speed Va2") when the target vehicle 1A passes through the position closest to the intersection CS2 during the time slot Ts from 6:00 PM to 7:00 PM. In the example shown in FIG. 8, the average traveling speed Va2 is assumed to be 50 m / s.

[0116] The prediction unit 23 then predicts the congestion passing time T1 at the intersection CS2 using the confirmed congestion length L and average traveling speed Va2. Specifically, for example, the prediction unit 23 calculates the congestion passing time T1 at the intersection CS2 by substituting the confirmed congestion length L and average traveling speed Va2 into equation (1). In the example shown in FIG. 8 , the congestion passing time T1 at the intersection CS2 is 2 seconds.

[0117] In addition, by referring to the signal-related table Tb3 shown in Figure 7, the prediction unit 23 confirms that the cycle length and split when the target vehicle 1A flows into the intersection CS2 from the entrance road 31D and exits to the exit road 41B during the time period Ts from 6:00 p.m. to 7:00 p.m. are "75 seconds" and "0.8", respectively.

[0118] The prediction unit 23 then calculates the green time Tg at the intersection CS2 by multiplying the confirmed cycle length and split. In the example shown in Fig. 8, the green time Tg at the intersection CS2 is 60 seconds. The prediction unit 23 then predicts the permissible passage time T2 at the intersection CS2 using the calculated green time Tg.

[0119] 8, the intersection CS2 is the second intersection CS targeted for the stop prediction process. In this case, the prediction unit 23 checks whether or not the target vehicle 1A was predicted to stop in the previous stop prediction process, i.e., in the stop prediction process at the intersection CS1, which is the first target intersection.

[0120] When the prediction unit 23 predicts that the target vehicle 1A will pass through the intersection CS1 without stopping, the prediction unit 23 predicts the green time Tg at the intersection CS2 as the permissible passing time T2 at the intersection CS2.

[0121] On the other hand, when the prediction unit 23 predicts that the target vehicle 1A will stop at the intersection CS1, it predicts the elapsed time Tc1 from when the target vehicle 1A stops at the intersection CS1.

[0122] For example, the storage unit 28 stores the distance between the intersections CS. When the prediction unit 23 predicts that the target vehicle 1A will stop at the intersection CS1, the prediction unit 23 checks the distance Q1 between the intersection CS1 and the intersection CS2, which is stored in the storage unit 28.

[0123] Then, the prediction unit 23 calculates the elapsed time Tc1 using the confirmed distance Q1 and the average traveling speed Va2.

[0124] The relationship between the elapsed time Tc1, the distance Q1, and the average traveling speed Va2 is expressed by the following equation (4): Tc1=Q1 / Va2 (4)

[0125] After calculating the elapsed time Tc1, the prediction unit 23 calculates the permissible passage time T2 using the green time Tg at the intersection CS2 and the calculated elapsed time Tc1.

[0126] The relationship between the permissible passing time T2, the green time Tg, and the elapsed time Tc1 is expressed by the following equation (5): T2=Tg−Tc1 (5)

[0127] 8, the distance Q1 is 2000 m. As described above, the average traveling speed Va2 is 50 m / s. In this case, the elapsed time Tc1 is 40 seconds.

[0128] As described above, the green time Tg at the intersection CS2 is 60 seconds, and the permissible passage time T2 at the intersection CS2 is 20 seconds.

[0129] After predicting the traffic jam passing time T1 and the permissible passing time T2 at the intersection CS2, the prediction unit 23 calculates the coefficient a in the formula (3) using the predicted traffic jam passing time T1 and permissible passing time T2 according to the calculation formula N. Here, the coefficient a is assumed to be −0.2.

[0130] When the traffic jam passing time T1 at intersection CS2, the allowable passing time T2 at intersection CS2, and the coefficient a are "2 seconds," "20 seconds," and "-0.2," respectively, the evaluation value H calculated using equation (3) is -2 seconds. In this case, the prediction unit 23 determines that the target vehicle 1A will pass through intersection CS2 without stopping, because the calculated evaluation value H is equal to or less than zero.

[0131] (c) Intersection CS3 Next, the prediction unit 23 predicts whether the target vehicle 1A will stop at intersection CS3.

[0132] More specifically, for example, by referring to the congestion length table Tb2 shown in Figure 6, the prediction unit 23 confirms that the congestion length L when the target vehicle 1A flows into the intersection CS3 from the entrance road 31D and then flows out to the exit road 41B during the time period Ts from 18:00 to 19:00 is "80 m".

[0133] Furthermore, the prediction unit 23 acquires, from the storage unit 28, average speed information C indicating the average traveling speed Va (hereinafter also referred to as "average traveling speed Va3") when the target vehicle 1A passes through the position closest to the intersection CS2 during the time slot Ts from 6:00 PM to 7:00 PM. In the example shown in FIG. 8, the average traveling speed Va3 is assumed to be 50 m / s.

[0134] Then, the prediction unit 23 predicts the congestion passing time T1 at the intersection CS3 using the confirmed congestion length L and average traveling speed Va3. Specifically, for example, the prediction unit 23 calculates the congestion passing time T1 at the intersection CS3 by substituting the confirmed congestion length L and average traveling speed Va into equation (1). In the example shown in FIG. 8 , the congestion passing time T1 at the intersection CS3 is 1.6 seconds.

[0135] In addition, by referring to the signal-related table Tb3 shown in Figure 7, the prediction unit 23 confirms that the cycle length and split when the target vehicle 1A flows into the intersection CS3 from the entrance road 31D and exits to the exit road 41B during the time period Ts from 6:00 p.m. to 7:00 p.m. are "50 seconds" and "0.8", respectively.

[0136] The prediction unit 23 then calculates the green time Tg at the intersection CS3 by multiplying the confirmed cycle length and split. In the example shown in Fig. 8, the green time Tg at the intersection CS3 is 40 seconds. The prediction unit 23 then predicts the permissible passage time T2 at the intersection CS2 using the calculated green time Tg.

[0137] 8, the intersection CS3 is the third intersection CS targeted for the stop prediction process. In this case, the prediction unit 23 checks whether or not the target vehicle 1A is predicted to stop in at least one of the stop prediction process at the intersection CS1, which is the first target intersection, and the stop prediction process at the intersection CS2, which is the second target intersection.

[0138] When the prediction unit 23 predicts that the target vehicle 1A will pass through both the intersection CS1 and the intersection CS2 without stopping, the prediction unit 23 predicts the green time Tg at the intersection CS3 as the permissible passage time T2 at the intersection CS3.

[0139] On the other hand, if the prediction unit 23 predicts that the target vehicle 1A will stop at intersection CS1 and pass through intersection CS2, it checks the distance Q1 between intersections CS1 and CS2, and the distance Q2 between intersections CS2 and CS3, which are stored in the memory unit 28.

[0140] Then, the prediction unit 23 uses the confirmed distances Q1, Q2 and average traveling speeds Va2, Va3 to calculate the elapsed time Tc2 since the target vehicle 1A stopped at the intersection CS1.

[0141] The relationship between the elapsed time Tc2, the distances Q1 and Q2, and the average traveling speeds Va2 and Va3 is expressed by the following equation (6): Tc2=Q1 / Va2+Q2 / Va3 (6)

[0142] After calculating the elapsed time Tc2, the prediction unit 23 calculates the permissible passage time T2 at the intersection CS3 using the green time Tg at the intersection CS2 and the calculated elapsed time Tc2.

[0143] The relationship between the permissible passage time T2, the green time Tg, and the elapsed time Tc2 at the intersection CS3 is expressed by the following equation (7): T2=Tg−Tc2 (7)

[0144] Furthermore, when the prediction unit 23 predicts that the target vehicle 1A will pass through the intersection CS1 and stop at the intersection CS2, it checks the distance Q2 between the intersections CS2 and CS3 stored in the memory unit 28.

[0145] Then, the prediction unit 23 uses the confirmed distance Q2 and the average traveling speed Va3 to calculate the elapsed time Tc3 since the target vehicle 1A stopped at the intersection CS2.

[0146] The relationship between the elapsed time Tc3, the distance Q2, and the average traveling speed Va3 is expressed by the following equation (8): Tc3=Q2 / Va3 (8)

[0147] After calculating the elapsed time Tc3, the prediction unit 23 calculates the permissible passage time T2 at the intersection CS3 using the green time Tg at the intersection CS2 and the calculated elapsed time Tc3.

[0148] The relationship between the permissible passage time T2, the green time Tg, and the elapsed time Tc3 at the intersection CS3 is expressed by the following equation (9): T2=Tg−Tc3 (9)

[0149] 8, the target vehicle 1A stops at the intersection CS1 and passes through the intersection CS2 without stopping. In this case, the prediction unit 23 calculates the elapsed time Tc2 using equation (6).

[0150] Specifically, the distance Q2 between intersections CS2 and CS3 is 3000 m. As described above, the distance Q1 is 2000 m, the average traveling speeds Va2 and Va3 are 50 m / s, and the elapsed time Tc2 is 100 seconds.

[0151] As described above, the green time Tg at intersection CS3 is 40 seconds. In this case, the permissible passage time T2 at intersection CS2 is the green time Tg (40 seconds) minus the elapsed time Tc2 (100 seconds), i.e., −60 seconds.

[0152] After predicting the traffic jam passing time T1 and the permissible passing time T2 at the intersection CS2, the prediction unit 23 calculates the coefficient a in the formula (3) using the predicted traffic jam passing time T1 and permissible passing time T2 according to the calculation formula N. Here, the coefficient a is assumed to be −0.1.

[0153] When the congestion passing time T1 at intersection CS3, the allowable passing time T2 at intersection CS3, and the coefficient a are "1.6 seconds," "-60 seconds," and "-0.1," respectively, the evaluation value H calculated using equation (3) is 7.6 seconds. In this case, the prediction unit 23 determines that the target vehicle 1A will stop at intersection CS3 because the calculated evaluation value H is greater than zero.

[0154] <Second Prediction> For example, in the stop prediction process, the prediction unit 23 further performs a second prediction to predict the stopping time of the target vehicle 1A at the target intersection.

[0155] More specifically, for example, the prediction unit 23 calculates an evaluation value H for each target intersection using equation (3), and if the calculated evaluation value H is greater than zero, predicts the evaluation value H as the stop time at the target intersection.

[0156] 8, as described above, the evaluation value H at the intersection CS1 and the evaluation value H at the intersection CS3 are 5 seconds and 7.6 seconds, respectively. In this case, the prediction unit 23 predicts the stopping times at the intersection CS1 and the intersection CS3 to be 5 seconds and 7.6 seconds, respectively.

[0157] <Third Prediction> For example, in the stop prediction process, the prediction unit 23 further performs a third prediction of predicting a target intersection at which the target vehicle 1A will stop, among a plurality of target intersections on the planned driving route of the target vehicle 1A.

[0158] More specifically, for example, the prediction unit 23 predicts a target intersection where the evaluation value H, which is the calculation result of equation (3), is greater than zero as a target intersection where the target vehicle 1A will stop.

[0159] 8, as described above, the intersection CS1 has an evaluation value H of 5 seconds, and the evaluation value H is 7.6 seconds. In this case, the prediction unit 23 predicts the stopping time at the intersection CS1 and the stopping time at the intersection CS3 to be 5 seconds and 7.6 seconds, respectively.

[0160] When the prediction unit 23 completes the stop prediction process, the prediction unit 23 outputs prediction result information indicating the prediction result to the creation unit 24 .

[0161] (Gradient Table) FIG. 9 is a diagram illustrating an example of a gradient table stored by the vehicle information prediction device according to the embodiment of the present disclosure.

[0162] Referring to Figure 9, for example, the memory unit 28 stores a gradient table Tb4 that indicates the correspondence between the position coordinates of the start point of a road link (hereinafter also referred to as the "start point coordinates"), the position coordinates of the end point of the road link (hereinafter also referred to as the "end point coordinates"), and the gradient W of the road link.

[0163] In the gradient table Tb4 shown in Fig. 9, the gradient W corresponding to the start point coordinates (X11, Y11) and the end point coordinates (X12, Y12) is "1.8%." The gradient W corresponding to the start point coordinates (X12, Y12) and the end point coordinates (X13, Y13) is "-0.5%." The gradient W corresponding to the start point coordinates (X14, Y14) and the end point coordinates (X15, Y15) is "2.2%."

[0164] (Gradient Information Acquisition Unit) Referring back to FIG. 3, for example, the gradient information acquisition unit 25 acquires gradient information indicating the gradient of the planned travel route indicated by the route information acquired by the vehicle information acquisition unit 21.

[0165] More specifically, for example, when receiving route information from the vehicle information acquisition unit 21, the gradient information acquisition unit 25 divides the planned travel route indicated by the route information into a plurality of road links. Then, by referring to the gradient table Tb4 in the storage unit 28, the gradient information acquisition unit 25 identifies, for each divided road link, the gradient W corresponding to the start point coordinates and the end point coordinates of the road link.

[0166] Then, the gradient information acquisition unit 25 outputs gradient information indicating the gradient W of each identified road link to the creation unit 24 .

[0167] (Creation unit) For example, the creation unit 24 performs a creation process to create time series data D of the speed of the target vehicle 1A when the target vehicle 1A travels along the planned travel route indicated by the route information, based on the prediction result of the stop prediction process by the prediction unit 23, the gradient information acquired by the gradient information acquisition unit 25, and the route information acquired by the vehicle information acquisition unit 21.

[0168] More specifically, for example, when the creation unit 24 receives route information from the vehicle information acquisition unit 21, the creation unit 24 acquires from the storage unit 28 a plurality of pieces of average speed information C that include the same vehicle ID as the vehicle ID indicated in the route information.

[0169] Then, the creation unit 24 selects, from the multiple pieces of average speed information C acquired from the memory unit 28, average speed information C1 that indicates the average driving speed Va during the period A including the departure time ta indicated by the route information received from the vehicle information acquisition unit 21, and that indicates the average driving speed Va at the position closest to the position coordinate G1 of the departure point indicated by the route information.

[0170] In addition, from among the multiple pieces of average speed information C acquired from the memory unit 28, the creation unit 24 selects, for each scheduled passage time tb indicated in the received route information from the vehicle information acquisition unit 21, average speed information C2 that indicates the average driving speed Va during the period A that includes the scheduled passage time tb, and that indicates the average driving speed Va at the position closest to the position coordinate G3 of the intersection CS that corresponds to the scheduled passage time tb.

[0171] In addition, the creation unit 24 selects, from the multiple average speed information C acquired from the memory unit 28, average speed information C3 that indicates the average driving speed Va during the period A including the estimated arrival time tc indicated by the route information received from the vehicle information acquisition unit 21, and that indicates the average driving speed Va at the position closest to the position coordinate G2 of the destination indicated by the route information.

[0172] When the creation unit 24 selects the average speed information C1, C2, C3, it corrects the average traveling speed Va indicated by the selected average speed information C for each piece of average speed information C using the gradient information received from the gradient information acquisition unit 25. Specifically, for example, the creation unit 24 checks the gradient W of the road link that is closest to the position of the target vehicle 1A indicated by the selected average speed information C, among the multiple gradients W indicated by the gradient information. Then, the creation unit 24 calculates the corrected average traveling speed Va by substituting the average traveling speed Va indicated by the average speed information C and the checked gradient W into a predetermined arithmetic expression.

[0173] Then, the creation unit 24 creates time-series data D using the corrected average traveling speed Va and the stopping time for the target vehicle 1A to stop at the target intersection, which is indicated by the prediction result information received from the prediction unit 23.

[0174] 10 is a diagram illustrating an example of time-series data of the speed of a target vehicle created in a creation process by a vehicle information prediction device according to an embodiment of the present disclosure. In Fig. 10, the horizontal axis represents time, and the vertical axis represents the average traveling speed Va [m / s] of the target vehicle 1A. Fig. 10 also illustrates time-series data D when the target vehicle 1A is predicted to pass through the intersections CS1, CS2, and CS3 shown in Fig. 8 in this order and to stop at the intersections CS1 and CS3.

[0175] Referring to FIG. 10, in the time series data D, times t2 and t6 are times when the target vehicle 1A stops at the intersections CS1 and CS3, respectively.

[0176] In the example shown in Figure 10, the period during which the target vehicle 1A is stopped at intersection CS1 and the period during which the target vehicle 1A is stopped at intersection CS3 are the period from time t2 to time t3 and the period from time t6 to time t7, respectively.

[0177] 10, the average traveling speed Va at time t1 between departure time ta and time t2 is 40 m / s. The average traveling speed Va at times t4 and t5 between time t3 and time t6, and the average traveling speed Va at time t8 between time t7 and estimated arrival time tc are 50 m / s.

[0178] Referring back to FIG. 3, after creating the time series data D, the creating unit 24 outputs the created time series data D to the calculating unit 26 .

[0179] (Specification Table) FIG. 11 is a diagram illustrating an example of a specification table stored by the vehicle information prediction device according to the embodiment of the present disclosure.

[0180] Referring to Figure 11, for example, the memory unit 28 stores a specification table Tb5 that indicates the correspondence between the name of vehicle 1, the model of vehicle 1, the weight of vehicle 1, the efficiency of the motor provided in vehicle 1 (hereinafter also referred to as "motor efficiency"), the rolling resistance of vehicle 1, and the air resistance of vehicle 1.

[0181] In the specification table Tb5 shown in FIG. 11 , the weight, motor efficiency, rolling resistance, and air resistance of a vehicle 1 with a vehicle name "U1" and a model number "AY2023" are "J1," "M1," "F1," and "Y1," respectively. The weight, motor efficiency, rolling resistance, and air resistance of a vehicle 1 with a vehicle name "U1" and a model number "AY2022" are "J2," "M2," "F2," and "Y2," respectively. The weight, motor efficiency, rolling resistance, and air resistance of a vehicle 1 with a vehicle name "U2" and a model number "BY2023" are "J3," "M1," "F3," and "Y3," respectively. The weight, motor efficiency, rolling resistance, and air resistance of a vehicle 1 with a vehicle name "U3" and a model number "CY2023" are "J4," "M3," "F2," and "Y2," respectively.

[0182] (Calculation unit) Referring again to Figure 3, for example, the calculation unit 26 performs a calculation process to calculate the energy consumption Ea of the target vehicle 1A when the target vehicle 1A travels along the planned travel route based on the prediction results of the stop prediction process by the prediction unit 23, the characteristics of the target vehicle 1A, and the running resistance of the target vehicle 1A.

[0183] For example, the storage unit 28 stores a vehicle table indicating the correspondence between the vehicle ID, the name of the vehicle 1, and the model of the vehicle 1.

[0184] When the calculation unit 26 receives route information from the vehicle information acquisition unit 21, it refers to the vehicle table in the storage unit 28 to confirm the name and model of the target vehicle 1A corresponding to the vehicle ID included in the route information.

[0185] Then, the calculation unit 26 refers to the specification table Tb5 in the storage unit 28 to confirm the weight, motor efficiency, rolling resistance, and air resistance corresponding to the confirmed vehicle name and model of the target vehicle 1A.

[0186] For example, the calculation unit 26 calculates the energy consumption Ea of the target vehicle 1A using the time series data D created in the creation process by the creation unit 24, i.e., the time series data D based on the prediction results of the stop prediction process by the prediction unit 23, and the weight, motor efficiency, rolling resistance and air resistance of the target vehicle 1A confirmed using the specification table Tb5.

[0187] Specifically, for example, the calculation unit 26 calculates the power consumption E10 of the target vehicle 1A when the target vehicle 1A travels along the planned travel route as the energy consumption Ea of the target vehicle 1A.

[0188] For example, the power consumption E10 is the sum of the power consumption E1 based on the average traveling speed Va, the power consumption E2 based on the characteristics of the target vehicle 1A, and the power consumption E3 based on the traveling resistance of the target vehicle 1A when the target vehicle 1A travels along the planned traveling route. That is, the relationship among the power consumption E10, the power consumption E1, the power consumption E2, and the power consumption E3 is expressed by the following equation (13): E10=E1+E2+E3 (13)

[0189] The characteristics of the target vehicle 1A are, for example, the weight and motor efficiency of the target vehicle 1A. The running resistance of the target vehicle 1A is, for example, the rolling resistance and air resistance of the target vehicle 1A.

[0190] Upon receiving the time series data D from the creation unit 24, the calculation unit 26 calculates the power consumption E1 by substituting the time series data D into a predetermined arithmetic expression.

[0191] 11, the calculation unit 26 checks the weight and motor efficiency of the target vehicle 1A, and then calculates the power consumption E2 by substituting the confirmed weight and motor efficiency into a predetermined calculation formula. Furthermore, the calculation unit 26 checks the rolling resistance and air resistance of the target vehicle 1A, and then calculates the power consumption E3 by substituting the confirmed rolling resistance and air resistance into a predetermined calculation formula.

[0192] Then, the calculation unit 26 substitutes the calculated power consumptions E1, E2, and E3 into equation (13) to calculate the power consumption E10.

[0193] After calculating the power consumption E10, the calculation unit 26 outputs calculation result information indicating the calculation result to the notification unit 27.

[0194] (Notification Unit) For example, the notification unit 27 notifies the user of the target vehicle 1A of the calculation result of the calculation process performed by the calculation unit 26.

[0195] More specifically, for example, when the notification unit 27 receives the calculation result information from the calculation unit 26, the notification unit 27 creates an IP packet P3 that includes the calculation result information and that includes, as a source IP address and a destination IP address, the IP address of the vehicle information prediction device 101 and the IP address of the in-vehicle device 201, which are stored in the storage unit 28. Then, the notification unit 27 notifies the created IP packet P3 to the in-vehicle device 201 via the external network 151 and the wireless base station device.

[0196] Referring back to FIG. 2, when the in-vehicle device 201 receives the IP packet P3 from the vehicle information prediction device 101, the in-vehicle device 201 transmits the calculation result information included in the received IP packet P3 to the navigation device 52A.

[0197] When the navigation device 52A receives the calculation result information from the in-vehicle device 201, the navigation device 52A performs a notification process based on the received calculation result information. Specifically, for example, the navigation device 52A displays the content indicated by the calculation result information on its own display unit. Note that the navigation device 52A may be configured to notify the user of the target vehicle 1A of the content indicated by the calculation result information by a method other than displaying it on its own display unit, for example, by voice.

[0198] [Operation Flow] Next, the operation flow of the vehicle information prediction device 101 in the vehicle information prediction system 501 according to the embodiment of the present disclosure will be described with reference to the drawings.

[0199] 12 and 13 are flowcharts defining an example of an operation procedure when the vehicle information prediction device according to the embodiment of the present disclosure performs stop prediction processing.

[0200] 12 and 13, first, vehicle information prediction device 101 waits for reception of route information from in-vehicle device 201 (NO in step ST101).

[0201] Then, when the vehicle information prediction device 101 receives route information from the in-vehicle device 201 (YES in step ST101), it identifies the intersection ID of a target intersection on the planned driving route indicated by the received route information. For example, as described above, the vehicle information prediction device 101 identifies the intersection ID corresponding to the position coordinate G3 of the intersection CS on the planned driving route indicated by the route information by referring to the intersection table Tb1 in the storage unit 28. Here, it is assumed that the vehicle information prediction device 101 identifies one intersection ID (step ST102).

[0202] Next, after identifying the intersection ID of the target intersection, the vehicle information prediction device 101 checks the incoming node and the outgoing node at the target intersection when the target vehicle 1A travels along the planned travel route. For example, as described above, the vehicle information prediction device 101 checks the incoming node and the outgoing node using the map information and the node table stored in the storage unit 28 (step ST103).

[0203] Next, the vehicle information prediction device 101 identifies the congestion length L at the target intersection. For example, as described above, the vehicle information prediction device 101 refers to the congestion length table Tb2 in the storage unit 28 to identify the intersection ID of the identified target intersection and the congestion length L corresponding to the confirmed incoming node and outgoing node (step ST104).

[0204] Next, the vehicle information prediction device 101 identifies the green time Tg at the target intersection. For example, as described above, the vehicle information prediction device 101 identifies the intersection ID of the identified target intersection and the cycle length and split corresponding to the confirmed incoming node and outgoing node by referring to the signal-related table Tb3 in the storage unit 28. Then, the vehicle information prediction device 101 calculates the green time Tg as a value obtained by multiplying the identified cycle length and split (step ST105).

[0205] Next, the vehicle information prediction device 101 acquires gradient information indicating the gradient of the planned driving route of the target vehicle 1A. For example, as described above, the vehicle information prediction device 101 divides the planned driving route into a plurality of road links and identifies the gradient W corresponding to each divided road link by referring to the gradient table Tb4 in the storage unit 28 (step ST106). Note that steps ST104, ST105, and ST106 may be executed in a reverse order or in parallel.

[0206] Next, the vehicle information prediction device 101 checks the average traveling speed Va of the target vehicle 1A when it travels along the planned traveling route. For example, as described above, the vehicle information prediction device 101 selects average traveling speed information Ca, which indicates the average traveling speed Va at the position closest to the target intersection during the time period Ts including the planned time tb of passing through the target intersection, from among the plurality of pieces of average traveling speed information C that are stored in the storage unit 28 and that include the same vehicle ID as the vehicle ID of the target vehicle 1A (step ST107).

[0207] Next, the vehicle information prediction device 101 predicts a traffic jam passing time T1 using the identified traffic jam length L and the average traveling speed Va indicated by the selected average speed information Ca (step ST108).

[0208] Next, the vehicle information prediction device 101 predicts the permissible passing time T2 using the identified green time Tg. Here, the vehicle information prediction device 101 predicts the green time Tg as the permissible passing time T2 (step ST109).

[0209] Next, after predicting the traffic jam passing time T1 and the permissible passing time T2, the vehicle information prediction device 101 calculates a coefficient a based on the predicted traffic jam passing time T1 and permissible passing time T2 according to the calculation formula N (step ST110).

[0210] Next, after calculating the coefficient a, the vehicle information prediction device 101 performs a stop prediction process. For example, as described above, the vehicle information prediction device 101 calculates the evaluation value H by substituting the predicted congestion passing time T1 and permissible passing time T2, and the calculated coefficient a into equation (3) (step ST111).

[0211] Next, the vehicle information prediction device 101 checks whether the calculated evaluation value H is greater than zero (step ST112).

[0212] If the calculated evaluation value H is greater than zero (YES in step ST112), the vehicle information prediction device 101 determines that the target vehicle 1A will stop at the target intersection (step ST113).

[0213] Next, the vehicle information prediction device 101 predicts the stopping time of the target vehicle 1A at the target intersection. For example, as described above, the vehicle information prediction device 101 predicts the calculated evaluation value H as the stopping time (step ST114).

[0214] Next, the vehicle information prediction device 101 creates time series data D of the speed of the target vehicle 1A when the target vehicle 1A travels along the planned driving route based on the prediction results of the stop prediction process and the acquired gradient information (step ST115).

[0215] Next, the vehicle information prediction device 101 uses the created time-series data D to calculate the power consumption E1 based on the average traveling speed Va when the target vehicle 1A travels along the planned traveling route (step ST116).

[0216] Next, the vehicle information prediction device 101 calculates the power consumption E2 based on the characteristics of the target vehicle 1A. For example, as described above, the vehicle information prediction device 101 identifies the weight and motor efficiency corresponding to the vehicle name and model of the target vehicle 1A by referring to the specification table Tb5 in the storage unit 28. Then, the vehicle information prediction device 101 calculates the power consumption E2 based on the identified weight and motor efficiency (step ST117).

[0217] Next, the vehicle information prediction device 101 calculates the power consumption E3 based on the running resistance of the target vehicle 1A. For example, as described above, the vehicle information prediction device 101 identifies the rolling resistance and air resistance corresponding to the vehicle name and model of the target vehicle 1A by referring to the specification table Tb5 in the storage unit 28. Then, the vehicle information prediction device 101 calculates the power consumption E3 based on the identified rolling resistance and air resistance (step ST118). Note that steps ST116, ST117, and ST118 may be executed in reverse order or in parallel.

[0218] Next, after calculating the power consumptions E1, E2, and E3, the vehicle information prediction device 101 calculates the power consumption E10 of the target vehicle 1A when the target vehicle 1A travels along the planned travel route. For example, as described above, the vehicle information prediction device 101 calculates the total value of the calculated power consumptions E1, E2, and E3 as the power consumption E10 (step ST119).

[0219] Next, the vehicle information prediction device 101 transmits calculation result information indicating the calculated power consumption E10 to the in-vehicle device 201 (step ST120), and waits for reception of new route information (NO in step ST101).

[0220] On the other hand, if the calculated evaluation value H is zero or less (NO in step ST112), the vehicle information prediction device 101 determines that the target vehicle 1A will not stop at the target intersection (step ST121) and creates time series data D of the driving speed of the target vehicle 1A (step ST115).

[0221] In the vehicle information prediction system 501 according to the embodiment of the present disclosure, the vehicle information prediction device 101 is configured to perform a first prediction for predicting whether the target vehicle 1A will stop at a target intersection, a second prediction for predicting the stopping time of the target vehicle 1A at the target intersection, and a third prediction for predicting the target intersection at which the target vehicle 1A will stop, in the stop prediction process. However, this is not limited to this. The vehicle information prediction device 101 may be configured to perform some of the first, second, and third predictions in the stop prediction process. Furthermore, the vehicle information prediction device 101 may be configured to perform another prediction regarding the stopping of the target vehicle 1A instead of some of the first, second, and third predictions, or in addition to the first, second, and third predictions, in the stop prediction process.

[0222] Furthermore, in the vehicle information prediction system 501 according to the embodiment of the present disclosure, the vehicle information prediction device 101 is configured to predict the traffic congestion passing time T1 and the allowable passing time T2 and perform the stop prediction process using the predicted traffic congestion passing time T1 and the allowable passing time T2, but this is not limited to this. The vehicle information prediction device 101 may be configured to predict either the traffic congestion passing time T1 or the allowable passing time T2. Furthermore, the vehicle information prediction device 101 may be configured to perform the stop prediction process using parameters other than the traffic congestion passing time T1 and the allowable passing time T2.

[0223] Furthermore, in the vehicle information prediction system 501 according to the embodiment of the present disclosure, the vehicle information prediction device 101 is configured to perform a creation process of creating time-series data D of the travel speed of the target vehicle 1A when the target vehicle 1A travels along the planned travel route based on the prediction result of the stop prediction process and the acquired gradient information, but this is not limited to this. The vehicle information prediction device 101 may be configured to perform the creation process based on the prediction result without using gradient information. Furthermore, the vehicle information prediction device 101 may be configured not to perform the creation process.

[0224] Furthermore, in the vehicle information prediction system 501 according to the embodiment of the present disclosure, the vehicle information prediction device 101 is configured to perform a calculation process to calculate the energy consumption Ea of the target vehicle 1A when the target vehicle 1A travels along the planned travel route based on the prediction result of the stop prediction process, but this is not limited to this. The vehicle information prediction device 101 may be configured not to perform the calculation process. In this case, for example, the vehicle information prediction device 101 transmits prediction result information indicating the prediction result of the stop prediction process to the in-vehicle device 201. Upon receiving the prediction result information from the vehicle information prediction device 101, the in-vehicle device 201 transmits the received prediction result information to the navigation device 52A.

[0225] Furthermore, some or all of the functions of the vehicle information prediction device 101 according to the embodiment of the present disclosure may be provided by cloud computing. That is, the vehicle information prediction device 101 according to the embodiment of the present disclosure may be a cloud server configured by a plurality of servers.

[0226] The above-described embodiments should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the above description, and is intended to include all modifications within the meaning and scope of the claims.

[0227] Each process (each function) in the above-described embodiments is realized by a processing circuit including one or more processors. The processing circuit may be configured as an integrated circuit or the like that combines one or more memories, various analog circuits, and various digital circuits in addition to the one or more processors. The one or more memories store programs (instructions) that cause the one or more processors to execute each of the processes. The one or more processors may execute each of the processes according to the program read from the one or more memories, or may execute each of the processes according to a logic circuit designed in advance to execute each of the processes. The processor may be any of various processors suitable for computer control, such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), and an ASIC (Application Specific Integrated Circuit). Note that the physically separated processors may cooperate with each other to execute the processes. For example, the processors installed in the physically separated computers may cooperate with each other via a network such as a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet to execute the processes. The program may be installed into the memory from an external server device or the like via the network, or may be distributed in a state stored on a recording medium such as a CD-ROM (Compact Disc Read Only Memory), a DVD-ROM (Digital Versatile Disc Read Only Memory), or a semiconductor memory, and then installed into the memory from the recording medium.

[0228] The above description includes the following additional features: [Supplementary Note 1] A vehicle information prediction device comprising: a route information acquisition unit that acquires route information indicating a planned driving route of a target vehicle; an intersection information acquisition unit that acquires intersection information related to intersections on the planned driving route indicated by the route information acquisition unit; and a prediction unit that performs stop prediction processing to predict whether the target vehicle will stop at the intersection, based on the intersection information acquired by the intersection information acquisition unit, wherein the vehicle information prediction device further comprises: a calculation unit that calculates energy consumption of the target vehicle when the target vehicle travels along the planned driving route, based on a prediction result of the stop prediction processing by the prediction unit, characteristics of the target vehicle, and the running resistance of the target vehicle.

[0229] [Supplementary Note 2] A vehicle information prediction device comprising a processing circuit, wherein the processing circuit acquires route information indicating a planned driving route of a target vehicle, acquires intersection information regarding intersections on the planned driving route indicated by the acquired route information, and performs a stop prediction process to predict whether the target vehicle will stop at the intersection based on the acquired intersection information.

[0230] REFERENCE SIGNS LIST 1 Vehicle 1A Target vehicle 11 In-vehicle communication unit 12, 26 Calculation unit 13 Out-vehicle communication unit 14, 28 Storage unit 21 Vehicle information acquisition unit 22 Intersection information acquisition unit 23 Prediction unit 24 Creation unit 25 Gradient information acquisition unit 26 Calculation unit 27 Notification unit 101 Vehicle information prediction device 151 External network 201 In-vehicle device 501 Vehicle information prediction system

Claims

1. A vehicle information prediction device comprising: a route information acquisition unit that acquires route information indicating a planned driving route of a target vehicle; an intersection information acquisition unit that acquires intersection information regarding intersections on the planned driving route indicated by the route information acquisition unit; and a prediction unit that performs stop prediction processing to predict whether the target vehicle will stop at the intersection based on the intersection information acquired by the intersection information acquisition unit.

2. The vehicle information prediction device according to claim 1, wherein the prediction unit predicts whether the target vehicle will stop at the intersection in the stop prediction process.

3. A vehicle information prediction device according to claim 1 or claim 2, wherein the prediction unit predicts the stopping time of the target vehicle at the intersection in the stopping prediction process.

4. A vehicle information prediction device as described in any one of claims 1 to 3, wherein the prediction unit, in the stop prediction process, predicts the intersection at which the target vehicle will stop, among the multiple intersections on the planned driving route.

5. A vehicle information prediction device as described in any one of claims 1 to 4, wherein the prediction unit further predicts a congestion passing time, which is the time required for the target vehicle to pass through the congestion at the intersection, based on the intersection information acquired by the intersection information acquisition unit, and the prediction unit performs the stop prediction processing using the predicted congestion passing time.

6. A vehicle information prediction device as described in any one of claims 1 to 5, wherein the prediction unit further predicts an allowable passing time, which is the time given to the target vehicle by a traffic light installed at the intersection for the target vehicle to pass through the intersection, based on the intersection information acquired by the intersection information acquisition unit, and the prediction unit performs the stop prediction processing using the predicted allowable passing time.

7. The vehicle information prediction device according to any one of claims 1 to 6, further comprising a creation unit that performs a creation process to create time series data of the speed of the target vehicle when the target vehicle travels along the planned travel route based on the prediction result of the stop prediction process by the prediction unit.

8. The vehicle information prediction device according to claim 7, further comprising a gradient information acquisition unit that acquires gradient information indicating the gradient of the planned driving route indicated by the route information acquired by the route information acquisition unit, and the creation unit performs the creation process further based on the gradient information acquired by the gradient information acquisition unit.

9. The vehicle information prediction device according to any one of claims 1 to 8, further comprising a calculation unit that calculates the energy consumption of the target vehicle when the target vehicle travels along the planned travel route based on the prediction result of the stop prediction process by the prediction unit.

10. A vehicle information prediction method in a vehicle information prediction device, comprising: a step of acquiring route information indicating a planned driving route of a target vehicle; a step of acquiring intersection information regarding intersections on the planned driving route indicated by the acquired route information; and a step of performing a stop prediction process that predicts whether the target vehicle will stop at the intersection based on the acquired intersection information.

11. A vehicle information prediction program used in a vehicle information prediction device, which causes a computer to function as: a route information acquisition unit that acquires route information indicating a planned driving route of a target vehicle; an intersection information acquisition unit that acquires intersection information regarding intersections on the planned driving route indicated by the route information acquisition unit; and a prediction unit that performs stop prediction processing to predict whether the target vehicle will stop at the intersection based on the intersection information acquired by the intersection information acquisition unit.

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