JP7456392B2 - Control equipment and control systems - Google Patents

Description

The present invention relates to a control device and a control system.

Patent Document 1 discloses that an image of a location that is difficult to see from the vehicle is received from a remote server, and it is determined whether there is an obstacle in the location. If there is an obstacle, the received image and the vehicle's own image are A method for controlling an automatically driving vehicle is disclosed in which an obstacle avoidance trajectory is generated from measurement data from a sensor or the like attached to the vehicle, and control is performed to avoid the obstacle.

Patent Document 2 discloses that when two or more vehicles are approaching an intersection where their travel routes intersect with each other, it is determined whether the vehicles will enter the intersection at a timing when the vehicles interfere with each other, and if there is interference, a passing priority is determined. A travel control method is disclosed in which a traffic ticket is sequentially issued to each vehicle to permit passage through the intersection in accordance with the priority order of traffic.

JP 2019-182402 Publication JP 2019-46013 Publication

The self-driving vehicle in Patent Document 1 uses measurement data from sensors installed on its own vehicle to recognize locations that are difficult to see, and also uses the actions of other vehicles to generate an obstacle avoidance trajectory. It is not possible to avoid obstacles in cooperation with other vehicles. That is, the traffic control system according to Patent Document 1 is a traffic control system that cooperates with the "recognition" of an autonomous vehicle.

In the travel management method of Patent Document 2, if a vehicle is unable to follow a traffic order using a pass ticket due to changes in the traffic environment or communication delays, for example, the traffic efficiency of the following vehicle is affected and traffic efficiency is reduced. do. Specifically, if a vehicle that receives a pass ticket from a server that performs traffic control slows down or takes evasive action to deal with a fallen object on the roadway, other vehicles waiting for the vehicle to pass will , Traffic efficiency decreases because a vehicle holding a pass ticket cannot pass through the intersection until it returns the pass ticket to the server after passing through the intersection and must continue to stop in front of the intersection.

In addition, since the determination of whether or not vehicles will interfere with each other at an intersection is made when the vehicles have stopped before the intersection, some vehicles, such as vehicles running on the priority road, that do not originally need to stop at the intersection may also It is necessary to stop at intersections, reducing traffic efficiency.

Furthermore, in Patent Document 2, the designer of the traffic control system predefines interference patterns and interference avoidance operations for vehicles at merging points, and the vehicles simply move according to the interference avoidance operations corresponding to the interference patterns received from the server. Therefore, the vehicle itself does not autonomously control what kind of interference avoidance action it should take. Furthermore, Patent Document 2 discloses that even if a disturbance factor occurs due to the potential uncertainty of the traffic environment, the vehicle attempts to avoid the disturbance factor by taking a route different from the route indicated by an interference avoidance operation based on its own judgment. It is not intended to control the movement of the object.

In other words, the functions of an autonomous vehicle are "recognition" to check the situation, "judgment" to decide what action to take based on the recognition results, and control the vehicle to take actions according to the judgment results. When classified into three types of "operations," the traffic control system according to Patent Document 2 is a traffic control system that cooperates with the "operations" of automatic driving vehicles.

By the way, as a way to avoid interference between vehicles at intersections without traffic lights, a control device generates virtual traffic rules as spatio-temporal constraints that spatio-temporally constrain local routes autonomously planned by autonomous vehicles. It is conceivable that the self-driving vehicle can run autonomously according to spatio-temporal constraints by transmitting the information to the self-driving vehicle.

However, for vehicles that cannot drive autonomously according to spatiotemporal constraints, it is difficult to avoid interference with other vehicles.

The present invention aims to provide a control device and a control system that can prevent interference with other vehicles in an interference area, even for vehicles that cannot drive autonomously according to time-space constraints.

The control device according to claim 1 includes: a reception unit that receives mobile body information representing the state of the mobile body from each of a plurality of mobile bodies; a global route representing a route to a destination for each mobile body; Generates a spatio-temporal constraint for each mobile body to follow in an interference region where global routes of a plurality of mobile bodies approach to a predetermined range, based on the mobile body information received by the reception unit for each mobile body. a control constraint generation unit that generates, for each moving body, a control constraint that constrains the control of the moving body based on the spatiotemporal constraints for each moving body; a transmission unit that transmits each of the control constraints to a mobile body associated with the control constraint, and the control constraint generation unit generates a speed limit for a section from the current position of the mobile body to the interference area. is calculated as the control constraint, and when the control constraint is less than a predetermined threshold, the distance from the current position of the moving object to the interference area is greater than or equal to the distance from the interference area to the predetermined stop position. If so, the control constraint is set to the threshold value .

The control device according to claim 2 includes a receiving unit that receives moving object information representing the state of the moving object from each of the plurality of moving objects; and the mobile object information received by the receiving unit, generate for each mobile object a spatio-temporal constraint to be followed by the mobile object in an interference region where global routes of a plurality of mobile objects approach to a predetermined range. a control constraint generation unit that generates a control constraint that restricts control of a moving body for each moving body based on the spatiotemporal constraint for each moving body; a transmitting unit that transmits each of the control constraints to a moving body associated with the control constraint, and the control constraint generating unit calculates the current target speed of the moving body as the control constraint; If the control constraint is less than the predetermined threshold, and the distance from the current position of the moving object to the interference area is greater than or equal to the distance from the interference area to the predetermined stop position, the control constraint is lower than the threshold. Set to .

The control device according to claim 3 includes a receiving unit that receives moving object information representing the state of the moving object from each of the plurality of moving objects, a global route representing the route to the destination of each moving object, and and the mobile object information received by the receiving unit, generate for each mobile object a spatio-temporal constraint to be followed by the mobile object in an interference region where global routes of a plurality of mobile objects approach to a predetermined range. a spatio-temporal constraint generation unit; a control constraint generation unit that generates a control constraint that restricts control of a movable body for each moving body based on the spatio-temporal constraint for each moving body; a transmitting unit that transmits each of the control constraints to a moving body associated with the control constraint, and the control constraint generating unit is configured to transmit the control constraint when a preceding vehicle with respect to the moving body is located in front of the interference area. A current target inter-vehicle distance of the moving object is calculated as the control constraint using a variable inter-vehicle time that changes depending on whether the moving object has passed through the interference area .

The control system according to claim 4 includes a receiving unit that receives, from each of a plurality of moving bodies, moving body information that indicates a state of the moving body, a spatio-temporal constraint generating unit that generates, for each moving body, a spatio-temporal constraint that the moving body should follow in an interference region where the global routes of the plurality of moving bodies approach each other to a predetermined range, based on a global route that indicates a route to a destination for each moving body and the moving body information received by the receiving unit for each moving body, a control constraint generating unit that generates control constraints that restrict control of the moving body, based on the time-space constraints for each moving body, and The system includes a control device having a transmitter that transmits to each moving body associated with a control constraint, and a moving body having a control unit that controls the movement of the moving body so as to satisfy the control constraint received from the control device, wherein the control constraint generation unit calculates a speed limit for the section from the current position of the moving body to the interference area as the control constraint, and if the control constraint is less than a predetermined threshold, and the distance from the current position of the moving body to the interference area is equal to or greater than the distance from the interference area to a predetermined stopping position, the control constraint is set to the threshold .
The control system described in claim 5 includes a control device including: a receiving unit that receives mobile body information representing a state of each of a plurality of moving bodies from each of the moving bodies; a space-time constraint generation unit that generates, for each moving body, a space-time constraint to be followed by the moving body in an interference area where the global routes of the plurality of moving bodies approach within a predetermined range based on a global route representing a route to a destination for each moving body and the moving body information received by the receiving unit for each moving body; a control constraint generation unit that generates a control constraint that constrains the control of the moving body based on the space-time constraint for each moving body; and a transmission unit that transmits each of the control constraints generated by the control constraint generation unit to a moving body associated with the control constraint; and a moving body including a control unit that controls the movement of the moving body so as to satisfy the control constraint received from the control device, wherein the control constraint generation unit calculates a current target speed of the moving body as the control constraint, and when the control constraint is less than a predetermined threshold, when the distance from the current position of the moving body to the interference area is equal to or greater than the distance from the interference area to a predetermined stopping position, the control constraint is set to the threshold.
The control system described in claim 6 includes a control device including: a receiving unit that receives mobile body information representing a state of each of a plurality of moving bodies from each of the moving bodies; a space-time constraint generation unit that generates, for each moving body, a space-time constraint to be followed by the moving body in an interference area where the global routes of the plurality of moving bodies approach each other to a predetermined range based on a global route representing a route to a destination for each moving body and the moving body information received by the receiving unit for each moving body; a control constraint generation unit that generates control constraints that constrain the control of the moving body based on the space-time constraint for each moving body; and a transmission unit that transmits each of the control constraints generated by the control constraint generation unit to a moving body associated with the control constraint; and a moving body including a control unit that controls the movement of the moving body so as to satisfy the control constraint received from the control device, wherein the control constraint generation unit calculates a current target vehicle distance of the moving body as the control constraint using a variable inter-vehicle time that changes depending on whether a preceding vehicle relative to the moving body is in front of the interference area or has passed through the interference area.

According to the present invention, even a vehicle that cannot autonomously travel according to spatio-temporal constraints can avoid interference with other vehicles in an interference area.

FIG. 1 is a diagram showing an example of a system configuration of a control system. FIG. 2 is a diagram illustrating an example of a functional configuration of a control device for an automated driving vehicle. FIG. 2 is a diagram showing an example of a functional configuration of a control device for a partially automated vehicle. It is a figure showing an example of functional composition of a control device. It is a figure which shows the example of the content of a vehicle control driving route map. FIG. 3 is a diagram showing an example of interference points. It is a figure showing an example of interference point information. FIG. 3 is a diagram showing an example of a global route. It is a diagram showing an example of a travel route. FIG. 2 is a diagram showing an example of the configuration of a main part of an electrical system in a control device. FIG. 2 is a diagram illustrating an example of a main part configuration of an electrical system in a vehicle control device. It is a flowchart which shows an example of the flow of control processing performed by CPU of a control device. FIG. 13 is a diagram showing an example of an interference time of a vehicle at an interference point. FIG. 2 is a schematic diagram illustrating virtual traffic rules at an interference point where the state s _x =0. FIG. 2 is a schematic diagram illustrating virtual traffic rules at an interference point set to the state s _x =1. It is a schematic diagram explaining cooperation of interference points. FIG. 3 is a diagram for explaining a case where the control constraint is a speed limit. FIG. 3 is a diagram for explaining a case where the control constraint is a speed limit. It is a flowchart which shows an example of the flow of the driving control process performed by CPU in the control device of an automatic driving vehicle. FIG. 13 is a diagram for explaining a case where the control constraint is a target velocity. FIG. 13 is a diagram for explaining a case where the control constraint is a target velocity. FIG. 3 is a diagram for explaining a case where the control constraint is a target inter-vehicle distance.

The present embodiment will be described below with reference to the drawings. Note that the same components and processes are given the same reference numerals throughout all the drawings, and redundant explanations will be omitted.

<First embodiment>

FIG. 1 is a diagram showing an example of a system configuration of a control system 1 according to the present embodiment. The control system 1 refers to traffic environment information obtained from sensors, etc., and controls a plurality of vehicles (the example in Fig. 1) that autonomously travels along a route 6 to a destination based on its own judgment. The vehicle P and the vehicle Q) include a plurality of wireless communication devices 3 and a control device 10 installed along a route 6, and each wireless communication device 3 is connected to the control device 10 through a communication network 5.

In this embodiment, vehicle P and vehicle Q are equipped with wireless equipment. Vehicles P and Q perform data communication with the control device 10 through the communication network 5 by wirelessly connecting to any one of the wireless communication devices 3 installed at the closest location, for example, while driving. Although the number of vehicles to be controlled by the control system 1 may be two or more, the method for controlling vehicles in the control system 1 will be described below, focusing on the vehicle P and the vehicle Q. Note that when it is not necessary to separately explain the plurality of vehicles to be controlled by the control system 1, the plurality of vehicles may be collectively referred to as "vehicles 7."

The wireless communication device 3 serves as a data relay device between the vehicle 7 and the control device 10. There are no restrictions on the installation location of the wireless communication device 3 as long as it is within the range where wireless communication with the vehicle 7 is possible; It is preferable to install it as close as possible to the roadway. Further, there is no restriction on the number of wireless communication devices 3 installed.

The communication network 5 transmits various information about the vehicle 7 received by the wireless communication device 3 to the control device 10, and transmits the control information about the vehicle 7 generated by the control device 10 to the wireless communication device 3 designated by the control device 10. to be transmitted. The control information is transmitted to all the wireless communication devices 3 according to instructions from the control device 10, and may be notified to each vehicle 7. Note that the communication network 5 may be a wired line or a wireless line.

The control device 10 operates at locations where the vehicles 7 approach to a predetermined range (hereinafter referred to as "interference range") where there is a risk of contact between the vehicles 7 without reducing the traffic efficiency of the vehicles 7, i.e. This is a device that performs traffic control to avoid interference at interference point X. Here, "interference" refers not only to a situation in which the vehicles 7 come into contact with each other, but also to a situation in which the vehicles 7 approach each other to the extent of interference.

The control device 10 receives moving object information representing the state of the vehicle 7 from each vehicle 7 to be controlled, and determines constraint conditions (hereinafter referred to as "virtual traffic rules") that constrain decisions regarding movement made by each vehicle 7. ) is generated for each vehicle 7 so that the vehicles 7 do not interfere with each other at the interference point do.

The vehicle 7 is classified into a plurality of types depending on the driving functions that the vehicle 7 has. In this embodiment, a case will be described in which the vehicle 7 is divided into a self-driving vehicle and a partially automated vehicle, as an example.

In this embodiment, the autonomous vehicle receives control constraints generated based on virtual traffic rules from the control device 10, and receives them while referring to traffic environment information obtained from sensors attached to the vehicle 7. This refers to a vehicle 7 equipped with an automatic driving function that runs while controlling the vehicle 7 based on its own judgment so as to satisfy control constraints and autonomously avoid interference between vehicles 7. Note that automatic driving according to the present embodiment refers to driving classified into automatic driving levels 3 to 5, in which the automatic driving function is responsible for driving rather than the driver. Note that in this embodiment, the automatic driving level refers to the level of automatic driving defined by the Society of Automotive Engineers (SAE).

A partially automated vehicle receives control constraints generated based on virtual traffic rules from the control device 10 and controls the movement of the vehicle to satisfy the received control constraints. It does not have an automatic driving function that allows the vehicle 7 to travel while controlling it based on its own judgment, but is a vehicle whose driving functions are partially automated. In this embodiment, a case will be described in which the partially automated driving vehicle is an automated driving level 1 or 2 vehicle equipped with a driving support function. An example of a partially automated vehicle includes, but is not limited to, a vehicle equipped with a cruise control function that automates at least one control of vehicle speed and inter-vehicle distance to a preceding vehicle.

Hereinafter, if there is no need to distinguish between an automated driving vehicle and a partially automated vehicle, they will be collectively referred to as "vehicle 7", and if they are explained separately, they will be referred to as an automated vehicle 7A and a partially automated vehicle, respectively. Let's call it 7B.

FIG. 2 is a diagram showing an example of the functional configuration of the control device of the automatic driving vehicle 7A.

As shown in FIG. 2, the control device of the autonomous vehicle 7A includes functional units, namely, a position estimation unit 21A, a state management unit 22A, a wireless communication unit 23A, a local route planning unit 24, and a control unit 25A, as well as a vehicle driving route map 26.

The position estimation unit 21A estimates the position of the automatic driving vehicle 7A. Specifically, the position estimating unit 21A determines the shape of the surrounding environment in which the automated driving vehicle 7A runs, such as a laser range finder or camera attached to the automated driving vehicle 7A, and the automated driving vehicle 7A that is a moving object. External shape data is acquired from an external sensor that captures the situation on lane 8, which is an example of the travel path of , the position of the automatic driving vehicle 7A is estimated using map matching that estimates a matching point as the current position of the automatic driving vehicle 7A. There are no restrictions on the method of estimating the position of the automatic driving vehicle 7A in the position estimation unit 21A, and other estimation methods may be used, such as estimating the position of the automatic driving vehicle 7A using GPS (Global Positioning System). The position estimation unit 21A notifies the state management unit 22A and the local route planning unit 24 of the estimated position of the automated driving vehicle 7A as position information.

The state management unit 22A manages vehicle-specific information including information used to uniquely identify the automatic driving vehicle 7A, such as number information and vehicle body number. The state management unit 22A also includes, for example, the position of the vehicle 7 represented by position information, and the surroundings acquired by a sensor (referred to as an "internal sensor") or an external sensor that measures the attitude, speed, control details, etc. Sensor values related to the traffic environment are collected in chronological order and managed as the state of the automatic driving vehicle 7A.

The status management unit 22A notifies the wireless communication unit 23A and the local route planning unit 24 of the vehicle-specific information and the status of the autonomously driven vehicle 7A as moving body information at least periodically and/or whenever necessary.

The wireless communication unit 23A transmits the mobile information received from the state management unit 22A to the control device 10 through the wireless communication device 3, and also transmits the control information received from the control device 10 through the wireless communication device 3 to local route planning without delay. Department 24.

The local route planning unit 24 uses the position information received from the position estimation unit 21A, the mobile information received from the state management unit 22A, the control information received from the wireless communication unit 23A, and the vehicle travel route map 26 to plan a local route. plan and notify the control unit 25A.

The local route planning unit 24 executes automatic driving included in the moving object information under the conditions that the autonomous vehicle 7A satisfies the control constraints included in the traffic control information and travels along the global route specified in the traffic control information. The condition of the road on which the vehicle is traveling is determined from the measurement data of an external sensor attached to the driving vehicle 7A, and an actual route is determined, such as which position on the road corresponding to the global route should be actually traveled. Then, the local route planning unit 24 sets the speed and attitude of the automatically driven vehicle 7A at each time to be followed in order to cause the vehicle 7 to travel along the determined actual route.

In this way, the actual route determined under the condition that the automated driving vehicle 7A travels along the global route designated by the control device 10 while satisfying the control constraints is called a "local route", and the local route includes , control details for causing the automatically driven vehicle 7A to travel along the local route are added. Note that the details of the global route and control constraints will be explained later.

The vehicle travel route map 26 includes map information representing the lane 8 in which the autonomous vehicle 7A travels, and is used to determine a local route and perform automatic driving at each time for the autonomous vehicle 7A to travel along the local route. It is used to set the control details of the vehicle 7A.

When the control unit 25A receives the local route from the local route planning unit 24, it controls the steering wheel, accelerator, brake, etc. of the automatic driving vehicle 7A according to the control contents included in the local route, and autonomously controls the automatic driving vehicle 7A along the local route. Realize driving.

The control contents of the automatic driving vehicle 7A, such as the operation amount of the steering wheel, accelerator, brake, etc. accompanying the control performed by the control unit 25A, are notified to the state management unit 22A through the respective internal sensors that measure various control amounts, and the automatic driving This is managed by the state management unit 22A as the state of the vehicle 7A.

Note that even if the autonomous vehicle 7A does not receive the global route from the control device 10, it is possible to judge the condition of the road on which it is traveling based on the measurement data of the external sensor and determine the actual route that satisfies the virtual traffic rules. However, as an example, it is assumed here that the automatic driving vehicle 7A receives the global route from the control device 10 and determines the actual route, that is, the local route.

FIG. 3 is a diagram showing an example of the functional configuration of the control device of the partially automated vehicle 7B.

As shown in FIG. 3, the control device of the partially automated vehicle 7B includes a position estimation section 21B, a state management section 22B, a wireless communication section 23B, a control section 25B, and a driving support control section 27.

Since the driver of the partially automated vehicle 7B determines the actual route, there is no local route planning section 24 and vehicle travel route map 26 as in the automated vehicle 7A, but instead there is a driving support control section 27. do.

The position estimating unit 21B estimates the position of the partially driven automated vehicle 7B using the same method as the position estimating unit 21A in the automated driving vehicle 7A, and notifies the state management unit 22B of the estimated position of the partially driven automated vehicle 7B as position information. do.

The state management unit 22B collects and manages moving body information of the partially driven automated vehicle 7B, similarly to the state managed unit 22A of the automated driving vehicle 7A. In the case of the partially automated vehicle 7B, since the driver controls the partially automated vehicle 7B, the state of the partially automated vehicle 7B includes the amount of operation of the steering wheel, accelerator, brake, etc. operated by the driver.

In addition, the state management unit 22B notifies the wireless communication unit 23B of the mobile object information to be managed at least either periodically or at any time.

The wireless communication unit 23B transmits the moving object information received from the state management unit 22B to the control device 10 through the wireless communication device 3, and performs driving support control on the control information received from the control device 10 through the wireless communication device 3 without delay. Department 27.

The driving support control unit 27 includes, for example, a cruise control function, and controls the speed of the partially automated vehicle 7B and the inter-vehicle distance to the preceding vehicle based on the control constraints included in the control information received from the control device 10 through the wireless communication unit 23B. control at least one of the following.

FIG. 4 is a diagram showing an example of the functional configuration of the control device 10.

As shown in FIG. 4, the control device 10 includes functional units, namely, a destination setting unit 11, a communication unit 12, a global route planning unit 13, a driving route prediction unit 14, an interference point identification unit 15, a virtual traffic rule generation unit 16, and a control constraint generation unit 17, as well as a controlled driving route map 18.

The communication unit 12 receives mobile information from each of the automated driving vehicle 7A and the partially automated vehicle 7B through the wireless communication device 3, and performs control based on virtual traffic rules generated by a virtual traffic rule generation unit 16, which will be described later. The control information including the control constraints generated by the constraint generation unit 17 is transmitted to each of the automatically driven vehicles 7A and the partially automated vehicles 7B designated as transmission destinations of the control information. The communication unit 12 is an example of a receiving unit that receives mobile object information, and is also an example of a transmitting unit that transmits control constraints.

Since the automatic driving vehicle 7A is a vehicle 7 that travels along a global route specified by the control device 10, the control device 10 needs to plan the global route.

Therefore, the destination setting unit 11 receives the destination to which the automated driving vehicle 7A is heading and the vehicle-specific information of the automated driving vehicle 7A, associates the vehicle-specific information of the automated driving vehicle 7A with the destination, and plans a global route. Notify Department 13.

Although the destination and vehicle-specific information of the automated driving vehicle 7A may be set in the destination setting unit 11 by the operator of the control device 10, the destination and vehicle specific information may be set by receiving moving body information including the destination from the automated driving vehicle 7A. The setting unit 11 may associate the vehicle-specific information included in the received moving object information with the destination and notify the global route planning unit 13 of the association.

The global route planning unit 13 plans a global route using the destination of the autonomous vehicle 7A received from the destination setting unit 11, the moving object information A of the autonomous vehicle 7A received from the communication unit 12, and the controlled driving route map 18. The global route planning unit 13 notifies the interference point identification unit 15 of the planned global route.

FIG. 5 is a diagram showing an example of the contents of the controlled travel route map 18 used for planning a global route. The controlled travel route map 18 is a road structure database expressed by a set of route information for each section, with the minimum unit being a section where traffic rules do not change.

Sections constituting the control driving route map 18 (in the example of FIG. 5, the section from point K1 to point K2, the section from point K2 to point K3, the section from point K3 to point K7, and the section from point K2 to point K5) , the section from point K4 to point K5, and the section from point K5 to point K6), each section has a speed limit, number of lanes, width, and priority lanes when lanes in the section intersect. Traffic rule information such as lane priority, such as whether the lane is a priority lane or a non-priority lane, is set.

Further, each section constituting the controlled driving route map 18 represents the route 6 of the vehicle 7 by a way point sequence that is a set of virtually set way points. A way point is one of the indicators representing the position of the vehicle 7 on the route 6, and since a way point ID is uniquely set for each way point, the way point ID indicates the position of the vehicle 7 on the route 6. is located.

Note that the controlled travel route map 18 includes interference point information that defines the interference point X in advance.

FIG. 6 is a diagram showing an example of the interference point X. For example, the interference point This includes the junction where 8A and non-priority lane 8B merge. The interference point X is a place where the vehicles 7 approach each other within the interference range, so the interference point X is not necessarily represented by a point, but by an area having a certain size. There is also. That is, the interference point X is an example of an interference area.

FIG. 7 is a diagram showing an example of interference point information, and the interference point information includes, for example, an interference point ID for uniquely identifying the interference point X for each interference point X, the position of the interference point The way point ID for uniquely identifying the corresponding way point, the position and way point ID of the way point one before the interference point X, and the priority lane 8A and non-priority lane 8B that share the same interference point This is information stipulated for each of the following.

The vehicle travel route map 26 for the automatic driving vehicle 7A described above is also configured with the same information as the controlled travel route map 18, but the vehicle travel route map 26 includes the control information necessary for the automatic driving vehicle 7A to actually travel. It may also include information that is not included in the driving route map 18.

Note that the controlled travel route map 18 does not necessarily need to include interference point information; in this case, the control device 10 may generate interference point information from the information on the way point sequence included in the controlled travel route map 18. good. Further, the controlled travel route map 18 does not necessarily need to be included in the control device 10, and the control device 10 may acquire the controlled travel route map 18 from an external device different from the control device 10 through the communication unit 12.

The global route planning unit 13 in FIG. 4 searches for a global route, which is a route 6 from the current position of the autonomous vehicle 7A to the destination on the controlled driving route map 18, for each autonomous vehicle 7A based on the vehicle-specific information and destination of the autonomous vehicle 7A received from the destination setting unit 11, the position information and vehicle-specific information of the autonomous vehicle 7A included in the mobile unit information A, and the controlled driving route map 18. In other words, the global route is represented as a route 6 connecting a series of via points in each section that constitutes the controlled driving route map 18.

FIG. 8 is a diagram showing an example of a global route of vehicle P and vehicle Q, both of which are automatically driven vehicles 7A. One route 6P connecting the current position of the vehicle P and the destination of the vehicle P from among the plurality of routes 6 is selected as the global route of the vehicle P, and one route 6P connects the current position of the vehicle Q and the destination of the vehicle Q. 6Q represents a situation where vehicle Q has selected the global route.

Note that the search method used by the global route planning unit 13 to search for a global route for the automatic driving vehicle 7A may be any method, and for example, a known search method such as Dijkstra search may be used.

Since the destination of the automatic driving vehicle 7A is determined in advance, the global route planning unit 13 can plan a global route for the automatic driving vehicle 7A, with the control device 10 taking the lead. Since the automatic driving vehicle 7A travels on a local route determined based on the global route, the control device 10 can know the route 6 of the automatic driving vehicle 7A based on the global route planned by itself.

On the other hand, in the case of the partially automated vehicle 7B, the driver determines the route 6, so the destination may not be determined in advance.

Therefore, the control device 10 cannot plan a global route for the partially automated vehicle 7B using the global route planning unit 13.

Instead, the control device 10 uses the travel route prediction unit 14 to predict the travel route of the partially automated vehicle 7B.

Specifically, the travel route prediction unit 14 predicts the travel route from the position information and speed included in the moving body information B of the partially automated vehicle 7B. For example, the traveling route prediction unit 14 assumes that the partially driven automated vehicle 7B travels from the current position to a specific distance (for example, 100 m) at a speed indicated by the moving object information B, and the section of the lane 8 that corresponds to the specific distance. The interference point identification unit 15 is notified of the route 6 that connects the series of waypoints as the travel route of the partially automated vehicle 7B.

However, since the destination of the partially-driving automated vehicle 7B is unknown, the driving route prediction unit 14 predicts all routes 6 that branch each time the lane 8 branches, unlike the global route when the lane 8 branches. The combination is treated as a travel route.

FIG. 9 is a diagram showing an example of a travel route when the vehicle P is a partially automated vehicle 7B. When the driving destination of the vehicle P branches into two as shown in FIG. and route 6P-2 are both travel routes of vehicle P.

Note that, as already explained, the mobile object information B of the partially automated vehicle 7B is acquired through the wireless communication device 3.

The interference point identification unit 15 determines each global route and each travel based on the global route of the automated vehicle 7A received from the global route planning unit 13 and the travel route of the partially driven automated vehicle 7B predicted by the travel route prediction unit 14. Identify the interference point X on the route. The interference point identification unit 15 notifies the virtual traffic rule generation unit 16 of the route 6 of each vehicle 7 and the interference point X on each route 6.

The virtual traffic rule generation unit 16 generates the moving object information of each vehicle 7 received from the communication unit 12, the route 6 of each vehicle 7 received from the global route planning unit 13, that is, the global route and the travel route, and the route 6 of each vehicle 7 received from the communication unit 12. Using the interference point X, virtual traffic rules for the automated driving vehicle 7A and the partially automated driving vehicle 7B are generated. Note that the virtual traffic rule is an example of a spatio-temporal constraint, and the virtual traffic rule generation unit 16 is an example of a spatio-temporal constraint generation unit.

Specifically, the virtual traffic rule generation unit 16 estimates the interference time t at each interference point X for the autonomous vehicle 7A and another vehicle 7 that interfere with each other at the interference point X on the global route of the autonomous vehicle 7A. However, at each interference point X, a passing priority order is defined for the automatic driving vehicle 7A such that interference between the vehicles 7 at the interference point X is avoided without reducing the traffic efficiency between the vehicles 7.

Similarly, the virtual traffic rule generation unit 16 estimates the interference time t at each interference point X for the partially driven automated vehicle 7B and the other vehicle 7 that interfere with each other at the interference point X on the travel route of the partially automated vehicle 7B. However, at each interference point X, a passing priority order is defined for the partially driven automated vehicle 7B such that interference between the vehicles 7 at the interference point X is avoided without reducing the traffic efficiency between the vehicles 7.

The virtual traffic rule generation unit 16 generates control information including a global route and virtual traffic rules for the autonomous vehicle 7A, and generates control information including virtual traffic rules for the partially automated vehicle 7B, and outputs the information to the control constraint generation unit 17.

The control constraint generation unit 17 generates control constraints that constrain the control of the automated driving vehicle 7A and the partially automated vehicle 7B based on the virtual traffic rules included in the control information generated by the virtual traffic rule generating unit 16. It is generated for each of the automated driving vehicle 7A and the partially automated vehicle 7B, and requests the communication unit 12 to transmit the control information associated with each of the automated driving vehicle 7A and the partially automated vehicle 7B. As a result, control information including global routes and control constraints associated with each of the automated driving vehicles 7A is transmitted from the wireless communication device 3 to each of the automated driving vehicles 7A, and to each of the partially automated driving vehicles 7B, Control information including control constraints associated with each will be transmitted.

Note that a detailed method for generating virtual traffic rules and a method for generating control constraints based on virtual traffic rules will be explained later.

Next, an example of the main part configuration of each electrical system in the control device 10 and the control device of the vehicle 7 will be described.

FIG. 10 is a diagram showing an example of the main part configuration of the electrical system in the control device 10. As shown in FIG. 10, the control device 10 is configured using a computer 30, for example.

The computer 30 includes a CPU (Central Processing Unit) 31, which is an example of a processor that processes each functional unit related to the control device 10 shown in Figure 4, a ROM (Read Only Memory) 32 that stores a control program that causes the computer 30 to function as the control device 10, a RAM (Random Access Memory) 33 used as a temporary work area for the CPU 31, a non-volatile memory 34, and an input/output interface (I/O) 35. The CPU 31, ROM 32, RAM 33, non-volatile memory 34, and I/O 35 are each connected via a bus 36.

The non-volatile memory 34 is an example of a storage device in which stored data is maintained even if the power supplied to the non-volatile memory 34 is cut off, and for example, a semiconductor memory is used, but a hard disk may also be used. Furthermore, the non-volatile memory 34 does not necessarily have to be built into the computer 30, and a portable storage medium that can be attached to and detached from the computer 30, such as a USB memory or a memory card, may also be used.

For example, a communication unit 37, an input unit 38, and a display unit 39 are connected to the I/O 35.

The communication unit 37 is connected to the communication network 5 and includes a communication protocol for performing data communication with the wireless communication device 3 and external devices connected to the communication network 5.

The input unit 38 is a unit that receives instructions from the operator of the control device 10 and notifies the CPU 31, and uses, for example, buttons, a touch panel, a keyboard, a mouse, and the like. If the instructions are given by voice, a microphone may be used as the input unit 38.

The display unit 39 is a device that displays information processed by the CPU 31, and may be, for example, a liquid crystal display, an organic EL (Electro Luminescence) display, or a projector.

In a situation where display of information is not required, such as when the control device 10 is installed in an unmanned data center or the like, the display unit 39 may not be connected to the I/O 35. Further, other units such as a printer unit may be connected to the I/O 35 as necessary.

On the other hand, FIG. 11 is a diagram showing an example of the main part configuration of the electrical system in the control device of the vehicle 7. As shown in FIG. Similarly to the control device 10, the control device of the vehicle 7 is also configured using, for example, a computer 40.

The computer 40 includes a CPU 41, which is an example of a processor that handles processing of each functional unit related to the control device of each type of vehicle 7 shown in FIGS. 2 and 3, and the computer 40 as a control device corresponding to each type of vehicle 7. It includes a ROM 42 that stores a control program to function, a RAM 43 used as a temporary work area for the CPU 41, a nonvolatile memory 44, and an I/O 45. A CPU 41, a ROM 42, a RAM 43, a nonvolatile memory 44, and an I/O 45 are connected to each other via a bus 46.

For example, a communication unit 47, an input unit 48, a display unit 49, an internal sensor 51, an external sensor 52, and a traveling device 53 are connected to the I/O 45.

The communication unit 47 includes a communication protocol for performing data communication with the wireless communication device 3.

The input unit 48 is a unit that receives instructions from the driver of the vehicle 7 and notifies the CPU 41, and uses, for example, a button, a touch panel, a pointing device, or the like. If the instructions are given by voice, a microphone may be used as the input unit 48.

The display unit 49 is a device that displays information processed by the CPU 41, and uses, for example, a liquid crystal display, an organic EL display, a projector, or the like.

The traveling device 53 is a device that adjusts the amount of operation of an operating device that affects the traveling state of the vehicle 7, such as a steering wheel, an accelerator, and a brake, and notifies the CPU 41 of the amount of operation as the state of the vehicle 7. In the case of the traveling device 53 of the automatic driving vehicle 7A, the amount of operation is controlled by the CPU 41. On the other hand, in the case of the traveling device 53 of the partially automated vehicle 7B, the CPU 41 controls the operation amount related to the cruise control function, for example, the operation amount related to the accelerator and the brake, and the operation amount related to the function not related to the cruise control function, e.g. The amount of operation of the steering wheel is controlled by the driver.

Note that the I/O 45 may be connected to a speaker that notifies the contents of the control information received from the control device 10 by voice, or a device that notifies by touch. The functions of the internal world sensor 51 and the external world sensor 52 are as described above, and for example, in the case of a vehicle 7 that does not have an automatic driving function such as a partially automated driving vehicle 7B, the external world sensor 52 is not necessarily connected to the I/O 45. Sometimes it hasn't been done.

Next, the operation of the control device 10 in the control system 1 will be described in detail.

FIG. 12 is a flowchart showing an example of the flow of control processing executed by the CPU 31 of the control device 10 when moving object information is received from each vehicle 7.

A control program that defines the control process is stored in advance in the ROM 32 of the control device 10, for example. The CPU 31 of the control device 10 reads a control program stored in the ROM 32 and executes control processing.

For ease of explanation, the following describes the operation of the control device 10 when the vehicles 7 to be controlled by the control system 1 are vehicles P and Q. However, even if there are three or more vehicles 7 to be controlled, the same processing can be performed for each vehicle 7.

In step S10, the CPU 31 determines the types of the vehicle P and the vehicle Q based on the driving function of the vehicle 7 notified by the moving object information.

In step S20, the CPU 31 determines whether the type of at least one of the vehicles P and Q determined in step S10 is an autonomous vehicle 7A. If at least one of the vehicles P and Q is an autonomous vehicle 7A, the process proceeds to step S30.

In step S30, the CPU 31 generates a global route for the automated driving vehicle 7A based on the location information of the vehicle 7 included in the moving body information received from the automated driving vehicle 7A and the destination of the automated driving vehicle 7A.

On the other hand, if it is determined in the determination process of step S20 that neither vehicle P nor vehicle Q is an autonomously driven vehicle 7A, the process proceeds to step S40 without executing the process of step S30.

In step S40, the CPU 31 determines whether the type of at least one of the vehicle P and the vehicle Q determined in step S10 is other than the automatic driving vehicle 7A, that is, the type of at least one of the vehicle P and the vehicle Q is partially automated. It is determined whether the vehicle is vehicle 7B. If the type of at least one of the vehicle P and the vehicle Q is the partially automated vehicle 7B, the process moves to step S50.

In step S50, the CPU 31 determines the travel route of the partially automated vehicle 7B based on the position information of the partially automated vehicle 7B included in the moving body information B received from the partially automated vehicle 7B and the speed of the partially automated vehicle 7B. generate.

In addition, if the speed of the partially-driving automated vehicle 7B is reduced at a point before a branch point, there is a higher probability that the partially-driving automated vehicle 7B will make a turn rather than going straight through the fork, so the CPU 31 determines that the partially-driving automated vehicle 7B will only take route 6 in the direction of the bend. It may also be generated as a travel route of 7B.

On the other hand, if it is determined in the determination process of step S40 that neither the vehicle P nor the vehicle Q is the partially automated vehicle 7B, the process proceeds to step S60 without executing the process of step S50.

In step S60, the CPU 31 selects a point where the way point indicating the route 6 of the vehicle P approaches within the interference range from the way point indicating the route 6 of the vehicle Q, that is, the point of interference X between the vehicles P and the vehicle Q. Identify. Specifically, the CPU 31 uses the global route or traveling route of the vehicle P acquired in step S30 or step S50 (simply referred to as "vehicle P route 6") and the global route of the vehicle Q acquired in step S30 or step S50. Or a waypoint where the driving routes (simply referred to as "vehicle Q's route 6") intersect or merge, or an area where vehicle P's route 6 and vehicle Q's route 6 run parallel to each other within the interference range. The way point is specified as the interference point X between vehicle P and vehicle Q.

Note that when the interference point information as shown in FIG. 7 can be referred to, the CPU 31 determines whether the same route set as the interference point X in the interference point information is added to each route 6 of vehicle P and vehicle If a point is included, the point represented by the way point may be set as the interference point X between vehicle P and vehicle Q.

In step S70, the CPU 31 calculates the interference time t between the vehicle P and the vehicle Q for each interference point X between the vehicle P and the vehicle Q identified in step S60.

The interference time t is determined by the distance along the route 6 from the current position of each of vehicles P and vehicle Q to the interference point X (hereinafter referred to as "distance to interference point It can be calculated from the speed. However, because vehicles P and Q cannot travel at a constant speed due to traffic conditions and the like, the speeds of vehicles P and Q actually vary.

For example, "v" is the respective speed of vehicle P and vehicle Q, "l" is the distance to interference point X, and "α" and "β" are positive margins corresponding to the speed changes of vehicle P and vehicle Q. , "ε" is a positive constant closer to 0 than other values, then the interference time t at the interference point X, taking into account the amount of variation in the speeds of the vehicle P and the vehicle Q, is calculated by equation (1).

Note that t _begin is the time when the vehicle P and the vehicle Q pass the interference point X earliest, and t _end is the time when the vehicle P and the vehicle Q pass the interference point X the latest. In this way, the interference time t at each interference point X of the vehicle P and the vehicle Q is expressed as a period greater than or equal to t _begin and less than t _end . Hereinafter, the interference time t of the vehicle P at the interference point X will be expressed as "interference time ^tp ", and the interference time t of the vehicle Q at the interference point X will be expressed as "interference time ^tq ". The method for calculating the interference time t is not limited to the above method, and prediction using a driver model or vehicle behavior model may be used.

FIG. 13 is a diagram showing an example of the interference time t ^p of the vehicle P and the interference time t ^q of the vehicle Q with respect to the specific interference point X.

In this way, the CPU 31 calculates the interference time t ^p of the vehicle P and the interference time t ^q of the vehicle Q for each interference point X.

In step S80, the CPU 31 sets the passing priority order of the vehicle P and the vehicle Q identified in step S60 at each interference point X.

Vehicles 7 whose passing priority is set to high, that is, whose passing priority is set to priority, can preferentially pass through the interference point X, and whose passing priority is set to low, that is, whose passing priority is set to non-priority. A constraint is set to prevent the vehicle 7 whose passing priority is set to priority from entering the interference point X during the interference time t at the interference point X.

In this way, by associating the passing priority order and the interference time t at each interference point interference will be avoided.

The correspondence between the passing priority order at each interference point No-entry times become virtual traffic rules. The virtual traffic rule is to restrict the time and place in which the vehicle 7 can travel using the interference time t and the position information of the interference point X so that the vehicle 7 does not interfere with other vehicles 7 at the interference point X. This is an example of a spatiotemporal constraint.

Hereinafter, the state indicating whether the passage priority at the interference point X is set to priority or non-priority will be expressed as "s _x ". _The state s _x at the interference point In this case, the state s _x =1 is set.

FIG. 14 is a schematic diagram illustrating virtual traffic rules at the interference point X where the state s _x =0.

In this case, if the interference time ^tp of vehicle P is expressed as a period from ^tp _begin to before ^tp _end , vehicle P can pass through the interference point X during the interference time ^tp in priority to vehicle Q. However, vehicle Q needs to avoid entering the interference point X during the interference time ^tp because there is a risk of interference with vehicle P if vehicle Q enters the interference point X during the interference time ^tp .

On the other hand, FIG. 15 is a schematic diagram illustrating the virtual traffic rules at the interference point X set to the state s _x =1.

In this case, if the interference time t ^q of vehicle Q is expressed as a period from t ^q _begin to less than t ^q _end , vehicle Q will give priority to interference point X over vehicle P during interference time t ^q . can be passed through. However, since the vehicle P may interfere with the vehicle Q if it enters the interference point X during the interference time ^tq , it is necessary to prevent the vehicle P from entering the interference point X during the interference time ^tq .

That is, the vehicle 7 whose passage priority at the interference point X is set to non-priority is prevented from entering the interference point X during the interference time t of the vehicle 7 whose passage priority at the same interference point By doing so, it is possible to avoid interference between the vehicles 7 and reach the destination.

In this way, the virtual traffic rule is a combination of the state s _x at the interference point X and the no-entry time at the interference point is expressed by information set for each interference point X.

At each interference point X, how to determine the passing priorities of vehicles P and vehicles Q is determined by evaluating the objective function C(S).

When there are N interference points X (N is a positive integer) between vehicles P and Q within the range managed by the control system 1, the set of states s _x at each interference point X is expressed as a state vector S = (s ₁ , s ₂ , _. . . s _i , .

The objective function C(S) is a function representing the transportation cost generated when the vehicle 7 moves to the destination. The transportation cost is a cost that takes into consideration not only the cost for each individual vehicle 7 but also the influence that the traveling of the vehicle 7 has on the traveling of other vehicles 7, and represents the traffic efficiency of the vehicle 7.

Here, C _single (S) is a cost that characterizes the traffic control for vehicles P and Q that pass through each interference point X. C _consistency (S) is a cost related to the consistency of passage priorities set for vehicle P and vehicle Q in the time direction. C _multi (S) is a cost related to consistency and interaction between interference points X. ω _c and ω _m are weights for adjusting the influence of C _consistency (S) and C _multi (S) on the objective function C(S).

The CPU 31 sets the state vector S so that the objective function C(S) is minimized. If the objective function C(S) is minimized, the traffic cost is also minimized, and the traffic efficiency in the area controlled by the control system 1 is improved.

Next, the objective function C(S) will be explained in detail.

C _single (S) is defined for the vehicles 7 (in this case, vehicles P and Q) passing through each interference point X, and is expressed by equations (3) and (4).

In other words, C _single (S) is obtained when the passing priority of the vehicle 7 whose interference time t at the interference point X is early for each interference point X, that is, the vehicle 7 whose expected passage time at the interference point Take the value that will be lower.

The CPU 31 calculates C _single (S) by considering the priority of the lane 8 in which the vehicle 7 travels, the length and traffic density of the vehicle line in the lane 8 in which the vehicle 7 travels, and the time the vehicle 7 has waited so far. Weighting may also be performed. For example, according to equation (4), although the passing priority of vehicle P is set to priority at a certain interference point X _i , the interference time t ^p of vehicle P is slower than the interference time t ^q of vehicle Q. In this case, C _single (s _i ;t ^p ,t ^q ) is set to “1”; however, if the vehicle P is traveling on a non-priority road, C _single (s _i ;t ^p , Weighting may be performed so that t ^q ) becomes a value larger than 1.

In other words, in situations where it would originally be better to set the priority passage order to priority, there are settings where the priority passage order is set to non-priority, and situations where it is considered better to set the priority passage order to non-priority. In this case, when the priority passage order is set to be prioritized, the CPU 31 may weight C _single (S) so that it becomes large.

In equation (2), C _consistency (S) representing the cost related to the consistency in the time direction of the passage priority set for the vehicle 7 is expressed by equations (5) and (6).

The state s _x is set to an optimal value in each predetermined time unit (for example, 1 second) along the time series with reference to the moving object information at that time, but s _x ^(T) is set at a time It represents the state s _x set at time T, and s _x ^(T-1) represents the state s _x set at a time one unit before time T.

The vehicle 7 can travel more efficiently in the lane 8 if the same vehicle 7 does not switch frequently between priority and non-priority indicating the passing priority order. Therefore, C _consistency (S) is set so that it becomes smaller if the temporally adjacent states s _x calculated repeatedly along the time series have the same value, and increases if they have different values. Take.

Here, as an example, the cost related to consistency when setting the state s _x ( ^T) is calculated from the relationship with the state s _x ^(T-1) set one unit ago, but the CPU 31, for example, The cost related to consistency when setting the state s _x ^(T) may be calculated from the relationship with the state s _x at each time up to M units ago (M is a positive integer of 2 or more). Specifically, the CPU 31 calculates the number of switches between priority and non-priority for each interference point C _consistency (S) may be calculated such that the value of C _consistency (S) increases as the time increases.

In equation (2), C _multi (S) representing the cost related to consistency and interaction between interference points X is expressed by equations (7) and (8).

Here, w _i,j is a value representing the degree of cooperation between the two interference points X _i and X _j and is set to w _i,j ≧0. The degree of cooperation of the interference points X refers to the degree to which it is considered that it is better to link the passage priority of one interference point X with the passage priority of the other interference point X and set them to the same state.

w _i,j = 0 indicates that there is no need for the interference points X _i and X _j to cooperate; as w _i,j increases, it is better to treat the two interference points X _i and X _j in cooperation. represents something.

FIG. 16 is a diagram showing an example in which it is better to handle a plurality of interference points X in a coordinated manner. As shown in FIG. 16, when a vehicle P traveling on a non-priority lane 8B crosses a priority lane 8A with one lane on each side where another vehicle 7 travels and goes straight, the vehicle P travels straight between the interference point X ₁ and the interference point It is necessary to pass X ₂ .

In such a situation, if the state _s1 of the vehicle P at the interference point _X1 is set to " ₀ " and the state _s2 of the vehicle P at the interference point X2 is set to " ₁ ", the vehicle P may stop in the priority lane 8A to avoid interference with another vehicle 7 at the interference point X2. In this case, the vehicle P will obstruct the flow of vehicles 7 traveling in the priority lane 8A.

Therefore, if there are multiple interference points X at locations where it is better not to stop until the vehicle 7 has finished passing once it has started passing, the state s _x of each interference point It is better to coordinate points X with each other.

w _i,j for each combination of interference points X is calculated in advance and stored in the nonvolatile memory 34, for example. The CPU 31 may calculate C _multi (S) by referring to w _i,j stored in the nonvolatile memory 34 .

Here _, as an example, we have explained the calculation of C _multi (S) based on the degree of cooperation between two interfering points Good too.

As shown in equation (2), the objective function C(S) is based on the content of air traffic control for the vehicle 7, the temporal consistency of the passage priority at the interference point X, and the consistency and interaction between the interference points X. However, the objective function C(S) used by the CPU 31 to determine the passing priority order of each vehicle 7 at the interference point X is not limited to equation (2).

For example, an objective function C(S) defined using the positional relationship of the vehicles 7 may be used such that a larger value is set as the positions of the plurality of vehicles 7 approach.

Further, the CPU 31 may determine the state vector S that minimizes the objective function C(S) by substituting all possible state vectors S into the objective function C(S). An optimization method may be used to determine the state vector S that minimizes the objective function C(S). Furthermore, if the influence of C _multi (S) on the objective function C(S) is less than the influence of C _single (S) and C _consistency (S) on the objective function C(S), the CPU 31 The state vector S that minimizes the objective function C(S) may be determined by dividing C(S) into subproblems by removing C _multi (S). Also, the state vector S is divided into S=(S _a ,S _b ), where S _a =(s ₁ ,...,s _i ), S _b =(s _i+1 ,...,s _N ) Then, if C _multi (S)=C _multi (S _a )+C _multi (S _b ), the CPU 31 converts the objective function C(S) into subproblems C(S _a ) and C(S _b ). The state vectors S _a and S _b that minimize each may be determined by dividing the state vectors.

In step S90 of FIG. 12, the CPU 31 determines whether or not virtual traffic rules are necessary according to the types of vehicles P and Q, and selects the vehicles 7 that require virtual traffic rules, that is, the automated driving vehicle 7A and the partially automated driving vehicle. 7B, each interference point X between vehicle P and vehicle Q identified in step S60, interference time t at each interference point X calculated in step S70, and state s _x at each interference point X set in step S80. Generate virtual traffic rules using Specifically, the CPU 31 uses position information representing the position of each interference point X (for example, interference point ID), the state s _x at each interference point _X , and each interference point A virtual traffic rule is generated in accordance with the types of vehicles P and Q, in which entry prohibition times are associated with each other.

In step S100, control constraints are generated based on the virtual traffic rules generated in step S90.

In the following, an example will be explained in which the vehicle P is prioritized, that is, the state s _x =0 by minimizing the objective function C(S). In this case, the vehicle Q is prohibited from entering the interference point X during the interference time t (period from t ^p _begin to less than t ^p _end ) during which the vehicle P is expected to pass the interference point X. Therefore, control constraints are calculated based on the time during which the vehicle Q is prohibited from entering at the interference point X, that is, the interference time t.

In this embodiment, the speed limit for the section from the current position of the vehicle Q to the interference point X is calculated as a control constraint.

As shown in FIG. 17(A), the distance from the current position of the vehicle Q to the interference point X is d, and the speed limit of the lane in which the vehicle Q is traveling is v _max .

As shown in FIG. 17(A), when the vehicle Q is traveling at the speed limit _vmax , the time _tx at which the vehicle Q passes the interference point X is expressed by the following equation.

_tx =d/ _vmax ...(9)

Then, when time t1 satisfies the following equation (10), a control constraint is generated that a speed limit is not set for vehicle Q.

t _x + α < t ^p _begin or t ^p _end < t _x - α (10)

Here, α is a time margin, that is, a margin, and is a predetermined fixed value.

That is, as shown in FIG. 17(B), when vehicle Q passes through interference point X earlier than the time t ^p _begin when vehicle P passes through interference point X earliest, or when vehicle P passes through interference point If the vehicle Q passes through the interference point X later than the time t ^p _end when the vehicle Q passes through the interference point X later, a control constraint is generated such that no speed limit is set for the vehicle Q.

On the other hand, as shown in FIG. 18(B), when the time tx at which the vehicle Q passes the interference point _X is during the interference time t, the speed limit v~ _max is calculated using the following equation as a control constraint.

v~ _max = d/(t ^p _end + β) ... (11)

Here, β is a time margin, that is, a margin, and is a predetermined fixed value. The relationship between α and β is preferably α≦β. Note that the speed limit v~ _max is calculated for each way point shown in FIG. As a result, as shown in FIGS. 18A and 18B, the vehicle Q passes the interference point X after the time t ^p _end +β when the vehicle P passes the interference point X latest. The speed limit v~ _max gradually decreases.

If the calculated speed limit v~ _max becomes less than a predetermined threshold _vmin , and the distance d from the current position of the vehicle Q to the interference point X is equal to or greater than the distance _dstop , the speed limit v~ _max may be set to _vmin . Here, the distance _dstop is the distance from the interference point X to a predetermined stop position (e.g., a stop line) ST, as shown in Fig. 18(A).

That is, when the calculated speed limit v~ _max becomes less than the threshold value v _min and the current position of the vehicle Q is before the stop position ST, there is a margin up to the interference point X, so the calculated speed limit v~ _max Set the threshold value v _min to be larger than . This can prevent the speed of the vehicle Q from becoming slower than necessary.

On the other hand, when the calculated speed limit v~ _max becomes less than the threshold value v _min , and the distance d from the current position of the vehicle Q to the interference point X is less than the distance d _stop , that is, the current position of the vehicle Q is stopped. If it exceeds the position ST, there is not enough time to reach the interference point X, so the calculated speed limit v~ _max is not set to the threshold value v _min and is left as is. For example, if the calculated speed limit v~ _max is 0, the vehicle Q will stop. This can prevent the vehicle Q from entering the interference point X and interfering with the vehicle P.

In step S110, the CPU 31 determines whether to generate control information and the content of the control information depending on the types of vehicles P and Q.

If the vehicle P or the vehicle Q is the automatic driving vehicle 7A, the CPU 31 generates control information including the global route and the control constraints calculated in step S100. If vehicle P or vehicle Q is partially automated vehicle 7B, CPU 31 generates control information including the control constraints calculated in step S100.

The CPU 31 performs control to transmit the generated control information to each of the vehicles 7 for which the control information is generated, through the wireless communication device 3 .

In this case, the CPU 31 grasps the position of the vehicle 7 for which control information is to be generated from the latest moving object information, and makes sure that the control information is transmitted from the wireless communication device 3 closest to the current position of the vehicle 7. , designates the wireless communication device 3 to which the control information is to be transmitted. The position information of the wireless communication device 3 is stored in advance in the nonvolatile memory 34, for example.

With the above, the control process shown in FIG. 12 is completed.

In addition, when the vehicles 7 to be controlled by the control device 10 are limited to the self-driving vehicle 7A and the partially-driving automated vehicle 7B, and the destination of the partially-driving automated vehicle 7B is clear, the global route is determined in the same way as the self-driving vehicle 7A. The planning unit 13 may plan a global route for the partially automated vehicle 7B. In this case, the travel route prediction unit 14 of the control device 10 is not required.

On the other hand, FIG. 19 is a flowchart showing an example of the flow of the travel control process executed by the CPU 41 in the control device of the automatic driving vehicle 7A when control information is received from the control device 10.

A control program that defines travel control processing for the automatic driving vehicle 7A is stored in advance in, for example, the ROM 42 in the control device of the automatic driving vehicle 7A. The CPU 41 in the control device of the automatic driving vehicle 7A reads a control program stored in the ROM 42 and executes travel control processing of the automatic driving vehicle 7A.

In step S200, the CPU 41 acquires the latest position information of the self-driving vehicle 7A.

In step S210, the CPU 41 acquires the latest moving object information in its own automatic driving vehicle 7A.

In step S220, the CPU 41 uses the position information of the automated driving vehicle 7A obtained in step S200, the moving object information A of the automated driving vehicle 7A obtained in step S210, and the control information received from the control device 10 to determine the local route. generate.

Specifically, the CPU 41 determines the relationship between other vehicles 7 based on the sensor value of the external sensor representing the situation on the lane 8 included in the moving object information A and the sensor value of the internal sensor representing the state of the automatic driving vehicle 7A. An actual route along the global route, that is, a local route, that can be traveled without interference is determined. At this time, the CPU 41 refers to the control constraints included in the air traffic control information, that is, the speed limit, and at the interference point X that is set as non-priority, the interference point The speed limit for the automatic driving vehicle 7A is determined so that the automatic driving vehicle 7A travels on a local route that does not allow the automatic driving vehicle 7A to enter.

In step S230, the CPU 41 operates the travel device, such as the steering wheel, accelerator, and brake, that controls the travel of the automatic driving vehicle 7A, in accordance with the control details for moving the automatic driving vehicle 7A along the local route generated in step S220. The automatic driving vehicle 7A is driven along the planned local route and according to the speed limit.

This completes the driving control process for the autonomous vehicle 7A shown in FIG. 19.

Note that when the vehicle 7 that has received the control information from the control device 10 is the partially automated vehicle 7B, the CPU 41 in the control device of the partially automated vehicle 7B performs the following steps instead of the processes in steps S220 and S230 in FIG. Execute processing. That is, the partially automated vehicle 7B is caused to travel at the speed limit as a control constraint included in the control information. As a result, the partially driven automated vehicle 7B travels during the prohibited entry time so as not to enter the interference point X associated with the prohibited entry time, thereby avoiding interference with other vehicles 7 at the interference point X. be able to.

<Second embodiment>

Next, a second embodiment will be described.

In the second embodiment, a case will be described in which a target speed is used as a control constraint. The configurations of the control system 1 and the control device 10 are the same as those in the first embodiment, so description thereof will be omitted.

In the second embodiment, the process of step S100 in the flowchart of the control process shown in FIG. 12 described in the first embodiment will be described. Processing other than step S100 is the same as in the first embodiment, so a description thereof will be omitted.

In the second embodiment, instead of the speed limit described in the first embodiment, the current target speed of the vehicle Q is calculated as the control constraint. Therefore, the speed limit described in the first embodiment can be replaced with the current target speed of the vehicle Q. A detailed description will be given below.

In step S100, control constraints are generated based on the virtual traffic rules generated in step S90.

In the following, an example will be explained in which the vehicle P is prioritized, that is, the state s _x =0 by minimizing the objective function C(S). In this case, the vehicle Q is prohibited from entering the interference point X during the interference time t (period from t ^p _begin to less than t ^p _end ) during which the vehicle P is expected to pass the interference point X. Therefore, control constraints are calculated based on the time during which the vehicle Q is prohibited from entering at the interference point X, that is, the interference time t.

In this embodiment, the current target speed of the vehicle Q is calculated as a control constraint. For example, the target speed is set as a target value for speed control of the automatic driving system. As another example, the target speed is set as a set speed for a cruise control function.

As shown in FIG. 20(A), the distance from the current position of the vehicle Q to the interference point X is d, and the target speed of the vehicle Q is v _r .

As shown in FIG. 20(A), when the vehicle Q is traveling at the target speed v _r , the time t _x at which the vehicle Q passes the interference point X is expressed by the following equation.

_tx =d/ _vr ...(12)

Then, when the time _tx satisfies the above equation (10), a control constraint is generated that the target speed is not set for the vehicle Q.

That is, as shown in FIG. 20(B), when vehicle Q passes through interference point X earlier than the time t ^p _begin when vehicle P passes through interference point X earliest, or when vehicle P passes through interference point If the vehicle Q passes through the interference point X later than the time t ^p _end when the vehicle Q passes through the interference point X later, a control constraint is generated in which the target speed is not set for the vehicle Q.

On the other hand, as shown in FIG. 21(B), when the time tx at which the vehicle Q passes the interference point _X is during the interference time t, the target speed is calculated using the following equation as a control constraint.

v _r = d/(t ^p _end + β) ... (13)

Here, β is a time margin, that is, a margin, and is a predetermined fixed value. The relationship between α and β is preferably α≦β. Note that the target speed v _r is calculated for each way point shown in FIG. 5 . As a result, as shown in FIGS. 21(A) and 21(B), the target is set so that the vehicle Q passes the interference point X after the time t ^p _end +β when the vehicle P passes the interference point X latest. The speed v _r gradually decreases.

In addition, when the calculated target speed v _r becomes less than the predetermined threshold value v _min and the distance d from the current position of the vehicle Q to the interference point X is greater than or equal to the distance d _stop , the target speed v _r is It may be set to _vmin .

In other words, if the calculated target speed v _r becomes less _than the threshold v _min and the current position of the vehicle Q is before the stop position ST, there is a margin up to the interference point Set a large threshold value v _min . This can prevent the speed of the vehicle Q from becoming slower than necessary.

On the other hand, when the calculated target speed v _r becomes less than the threshold v _min and the distance d from the current position of the vehicle Q to the interference point X is less than the distance d _stop , that is, the current position of the vehicle Q is the stop position. If ST is exceeded, there is not enough time to reach the interference point X, so the calculated target speed v _r is not set to the threshold value v _min and is left as is. For example, if the calculated target speed v _r is 0, the vehicle Q will stop. Thereby, the vehicle Q can be prevented from entering the interference point X and interfering with the vehicle P.

<Third embodiment>

Next, a third embodiment will be described.

In the third embodiment, a case will be described in which a target inter-vehicle distance is used as a control constraint. The configurations of the control system 1 and the control device 10 are the same as those in the first embodiment, so description thereof will be omitted.

In the third embodiment, the process of step S100 in the flowchart of the control process shown in FIG. 12 described in the first embodiment will be described. Processing other than step S100 is the same as in the first embodiment, so a description thereof will be omitted.

In the third embodiment, instead of the speed limit described in the first embodiment and the target speed described in the second embodiment, the current target inter-vehicle distance of vehicle Q is calculated as the control constraint.

In step S100, control constraints are generated based on the virtual traffic rules generated in step S90.

In the following, an example will be explained in which the vehicle P is prioritized, that is, the state s _x =0 by minimizing the objective function C(S). In this case, the vehicle Q is prohibited from entering the interference point X during the interference time t (period from t ^p _begin to less than t ^p _end ) during which the vehicle P is expected to pass the interference point X. Therefore, control constraints are calculated based on the time during which the vehicle Q is prohibited from entering at the interference point X, that is, the interference time t.

In this embodiment, the current target inter-vehicle distance of vehicle Q is calculated as a control constraint.

Here, as shown in FIG. 22, a target inter-vehicle distance L _des for maintaining a constant inter-vehicle distance between the vehicle Q and the preceding vehicle R is defined by the following equation.

L _des = (h+h~)v+L _safe ...(14)

Here, h is the inter-vehicle time. Inter-vehicle time is the time from when preceding vehicle R passes a certain point until vehicle Q passes that point. h~ is the variable inter-vehicle time due to control constraints. v is the speed of vehicle Q. _Lsafe is a predetermined fixed value. From the above formula (15), _Lsafe is the target inter-vehicle distance when (h+h~) or v is 0.

When no control constraints are transmitted from the control device 10 to the vehicle Q, h~=0.

When the vehicle Q is traveling at a speed v, the time _tx at which the vehicle Q passes the interference point X is expressed by the following equation.

t _x = d/v (15)

When the time _tx satisfies the above formula (10), a control constraint is generated such that a target inter-vehicle distance is not set for the vehicle Q.

On the other hand, when vehicle Q is traveling at speed v while keeping the inter-vehicle distance constant, and time tx when vehicle Q passes interference point _X is during interference time t, as shown in FIG. 22(A), When the preceding vehicle R is located in front of the interference point X, the variable inter-vehicle time h~ is calculated by the following formula.

h~ = (t ^p _end - t _now ) + β - (h + L _safe / v + L _a / v) ... (16)

Here, t _now is the current time. L _a is the distance from the rear of the preceding vehicle R to the interference point X, as shown in FIG. 22(A). β is a time margin, that is, a margin, and is a predetermined fixed value. The relationship between α and β is preferably α≦β.

On the other hand, when vehicle Q is traveling at speed v while keeping the inter-vehicle distance constant, and time tx when vehicle Q passes interference point _X is during interference time t, as shown in FIG. 22(B), When the preceding vehicle R has passed the interference point X, the variable inter-vehicle time h~ is calculated by the following formula.

h~=(t ^p _end -t _now )+β-(h+L _safe /v-L _a /v) ...(17)

22C, vehicle P passes the interference point X before vehicle Q passes the interference point X, and thus interference is avoided. In this manner, the target inter-vehicle distance _Ldes is determined by the variable inter-vehicle time h~ that changes according to the positional relationship between the preceding vehicle R and the interference point X, so interference with another vehicle 7 at the interference point X can be avoided while maintaining an appropriate inter-vehicle distance from the preceding vehicle R.

In each of the above embodiments, an example in which the control system 1 controls the vehicle 7 has been described, but the control target of the control system 1 is not limited to the vehicle 7, and may be any moving object. . Mobile objects include pedestrians, bicycles, motorcycles, wheelchairs, kickboards, ships, and flying objects such as drones.

Although the present invention has been described above using the embodiments, the present invention is not limited to the scope described in the embodiments. Various changes or improvements can be made to the embodiments without departing from the gist of the present invention, and forms with such changes or improvements are also included within the technical scope of the present invention. For example, the order of processing may be changed without departing from the gist of the present invention.

In the embodiment, an example has been described in which the control process in the control device 10 and the travel control process in the automatic driving vehicle 7A are realized by software, but the process equivalent to the flowcharts shown in FIGS. It may be implemented in an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a PLD (Programmable Logic Device) and processed by hardware. In this case, compared to the case where each process is realized by software, the processing speed can be increased.

In this way, the CPU 31 of the control device 10 and the CPU 41 of the control device of the vehicle 7 can be replaced by a dedicated processor specialized for specific processing, such as an ASIC, FPGA, PLD, GPU (Graphics Processing Unit), and FPU (Floating Point Unit). may be replaced with

Further, the operations of the CPU 31 of the control device 10 and the CPU 41 of the control device of the vehicle 7 in the embodiment may be realized by a plurality of CPUs 31 and a plurality of CPUs 41, in addition to being realized by one CPU 31 and one CPU 41, respectively. . Furthermore, the operations of the CPU 31 of the control device 10 and the CPU 41 of the control device of the vehicle 7 in the embodiment are realized by the cooperation of the CPU 31 of the computer 30 and the CPU 41 of the computer 40, which are located at physically separate locations. It may be.

In the above embodiment, the control program read by the CPU 31 of the control device 10 is installed in the ROM 32, and the control program read by the CPU 41 in the control device of the vehicle 7 is installed in the ROM 42, but this is not limiting. The control program and control program of the present invention can also be provided in a form recorded on a computer-readable storage medium. For example, the control program and control program may be provided in a form recorded on an optical disc such as a CD (Compact Disc)-ROM or a DVD (Digital Versatile Disc)-ROM. The control program and control program may also be provided in a form recorded on a portable semiconductor memory such as a Universal Serial Bus (USB) memory or a memory card.

Furthermore, the control device 10 and the control device of the vehicle 7 may download the control program and the control program, respectively, from an external device connected to the communication network 5.

1 Control system 7A Autonomous driving vehicle 7B Partially automated driving vehicle 10 Control device 11 Destination setting unit 12 Communication unit 13 Global route planning unit 14 Travel route prediction unit 15 Interference point identification unit 16 Virtual traffic rule generation unit 17 Control constraint generation unit 18 Controlled travel route map 21A, 21B Position estimation unit 22A, 22B State management unit 23A, 23B Wireless communication unit 24 Local route planning unit 25A, 25B Control unit 26 Vehicle travel route map 27 Driving assistance control unit

Claims (6)

a receiving unit that receives mobile body information representing the state of the mobile body from each of the plurality of mobile bodies;
The global routes of the plurality of moving objects approach each other within a predetermined range based on the global route representing the route to the destination of each moving object and the moving object information received by the receiving unit for each moving object. a spatio-temporal constraint generator that generates spatio-temporal constraints to be followed by the movable body in the interference region for each movable body;
a control constraint generation unit that generates control constraints that constrain control of the moving body for each moving body based on the spatio-temporal constraints for each moving body;
a transmitter that transmits each of the control constraints generated by the control constraint generator to a mobile object associated with the control constraint;
Equipped with
The control constraint generation unit calculates a speed limit for a section from the current position of the moving body to the interference area as the control constraint, and when the control constraint is less than a predetermined threshold, the current position of the moving body is calculated. If the distance from to the interference area is greater than or equal to the distance from the interference area to a predetermined stop position, the control constraint is set to the threshold value.
Control device. a receiving unit that receives mobile body information representing the state of the mobile body from each of the plurality of mobile bodies;
The global routes of the plurality of moving objects approach each other within a predetermined range based on the global route representing the route to the destination of each moving object and the moving object information received by the receiving unit for each moving object. a spatio-temporal constraint generator that generates spatio-temporal constraints to be followed by the movable body in the interference region for each movable body;
a control constraint generation unit that generates control constraints that constrain control of the moving body for each moving body based on the spatio-temporal constraints for each moving body;
a transmitter that transmits each of the control constraints generated by the control constraint generator to a mobile object associated with the control constraint;
Equipped with
The control constraint generation unit calculates the current target speed of the moving object as the control constraint, and when the control constraint is less than a predetermined threshold, the distance from the current position of the moving object to the interference area is calculated. , if the distance from the interference area to the predetermined stop position is greater than or equal to the distance, the control constraint is set to the threshold value.
Control device. a receiving unit that receives mobile body information representing the state of the mobile body from each of the plurality of mobile bodies;
The global routes of the plurality of moving objects approach each other within a predetermined range based on the global route representing the route to the destination of each moving object and the moving object information received by the receiving unit for each moving object. a spatio-temporal constraint generator that generates spatio-temporal constraints to be followed by the movable body in the interference region for each movable body;
a control constraint generation unit that generates control constraints that constrain control of the moving body for each moving body based on the spatio-temporal constraints for each moving body;
a transmitter that transmits each of the control constraints generated by the control constraint generator to a mobile object associated with the control constraint;
Equipped with
The control constraint generation unit determines the current state of the moving body using a variable inter-vehicle time that changes depending on whether a vehicle preceding the moving body is in front of the interference area or passing through the interference area. The target inter-vehicle distance is calculated as the control constraint.
Control device. a receiving unit that receives mobile unit information representing the state of the mobile unit from each of a plurality of mobile units ; a global route representing a route to a destination for each mobile unit; a spatio-temporal constraint generation unit that generates, for each mobile body, spatio-temporal constraints to be followed by the mobile bodies in an interference region where the global routes of the plurality of mobile bodies approach to a predetermined range, based on the body information ; a control constraint generation unit that generates a control constraint that constrains control of a moving body based on the spatio-temporal constraints of each time , and each of the control constraints generated by the control constraint generation unit is associated with the control constraint. a control device comprising a transmitting unit that transmits data to each mobile object;
a mobile body comprising a control unit that controls movement of the mobile body so as to satisfy the control constraints received from the control device;
including;
The control constraint generation unit calculates a speed limit for a section from the current position of the moving body to the interference area as the control constraint, and when the control constraint is less than a predetermined threshold, the current position of the moving body is calculated. If the distance from to the interference area is greater than or equal to the distance from the interference area to a predetermined stop position, the control constraint is set to the threshold value.
control system. a receiving unit that receives mobile unit information representing the state of the mobile unit from each of a plurality of mobile units; a global route representing a route to a destination for each mobile unit; a spatio-temporal constraint generation unit that generates, for each mobile body, spatio-temporal constraints to be followed by the mobile bodies in an interference region where the global routes of the plurality of mobile bodies approach to a predetermined range, based on the body information; a control constraint generation unit that generates a control constraint that constrains control of a moving body based on the spatio-temporal constraints of each time, and each of the control constraints generated by the control constraint generation unit is associated with the control constraint. a control device comprising a transmitting unit that transmits data to each mobile object;
a mobile body comprising a control unit that controls movement of the mobile body so as to satisfy the control constraints received from the control device;
including;
The control constraint generation unit calculates the current target speed of the moving object as the control constraint, and when the control constraint is less than a predetermined threshold, the distance from the current position of the moving object to the interference area is calculated. , if the distance from the interference area to the predetermined stop position is greater than or equal to the distance, the control constraint is set to the threshold value.
Control system. a receiving unit that receives mobile unit information representing the state of the mobile unit from each of a plurality of mobile units; a global route representing a route to a destination for each mobile unit; a spatio-temporal constraint generation unit that generates, for each mobile body, spatio-temporal constraints to be followed by the mobile bodies in an interference region where the global routes of the plurality of mobile bodies approach to a predetermined range, based on the body information; a control constraint generation unit that generates a control constraint that constrains control of a moving body based on the spatio-temporal constraints of each time, and each of the control constraints generated by the control constraint generation unit is associated with the control constraint. a control device comprising a transmitting unit that transmits data to each mobile object;
a mobile body comprising a control unit that controls movement of the mobile body so as to satisfy the control constraints received from the control device;
including;
The control constraint generation unit determines the current state of the moving body using a variable inter-vehicle time that changes depending on whether a vehicle preceding the moving body is in front of the interference area or passing through the interference area. The target inter-vehicle distance is calculated as the control constraint.
control system.