Control equipment and control system

Abstract

To realize a safe traffic flow by suppressing interference between moving objects with each other in an interference area even if a state regarding a movement of a moving object is different from the predicted state.SOLUTION: A control device 10 receives moving object information representing a state of each of vehicles 7 including an autonomous vehicle; detects whether or not an abnormality related to a movement of a manual vehicle has occurred from a change in a state of the manual vehicle that may interfere with the autonomous vehicle at an interference point X among each of the vehicles 7 which has received the moving object information; and generates a reinforced virtual traffic rule for the autonomous vehicle that is more restricted than a virtual traffic rule generated before the abnormality was detected so that the manual vehicle is less likely to interfere with the autonomous vehicle at the interference point X when the abnormality is detected.SELECTED DRAWING: Figure 23

Description

[Technical field]

[0001] The present invention relates to a control device and a control system. [Background technology]

[0002] Patent Document 1 discloses a driving control method in which an integrated control server sequentially manages, via communication, any status of multiple unmanned vehicles traveling along a specified driving route, and when two or more vehicles approach an intersection where their driving routes intersect, determines whether the vehicles will enter at a time when they will interfere with each other by merging, and if it is determined that the vehicles will enter the intersection, determines the passing priority of each vehicle that will be the subject of merging interference based on a predetermined order.

[0003] Patent Document 2 discloses a vehicle management system in which unmanned and manned vehicles are connected to a control station that controls them so that they can communicate with each other, and when the manned vehicle's on-board control device determines that there is an abnormality in the position notification to the control station, it generates a command to output a warning to the driver of the manned vehicle requesting that he stop, and when the control station recognizes an abnormality in the position notification from the manned vehicle, it generates a no-entry area for unmanned vehicles in abnormal situations, centered on the manned vehicle. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2019-46013 A [Patent Document 2] JP 2020-197885 A Summary of the Invention [Problem to be solved by the invention]

[0005] The vehicle travel control method in Patent Document 1 is a technology that assumes that the vehicle operates within the range predicted by the integrated control server. However, when the vehicle is actually traveling, for example, the vehicle may suddenly decelerate due to the detection of an obstacle, and the vehicle may operate in a manner that deviates from the prediction of the integrated control server. Therefore, in the vehicle travel control method shown in Patent Document 1, if the vehicle operates in a manner that deviates from the prediction of the integrated control server, the vehicles may interfere with each other at an intersection.

[0006] Furthermore, in the vehicle management system in Patent Document 2, when the control station recognizes an abnormality in the position notification from a manned vehicle, it generates an abnormality unmanned vehicle no-entry area that is centered on the manned vehicle and prohibits unmanned vehicles from entering that area. Therefore, for example, when a manned vehicle approaches an unmanned vehicle that has already entered an interference area, the manned vehicle and the unmanned vehicle may interfere with each other in the interference area, no matter how many abnormality unmanned vehicle no-entry areas have been generated for the manned vehicle.

[0007] Furthermore, unmanned vehicles cannot enter no-entry areas for unmanned vehicles under emergency conditions. Therefore, if an no-entry area for unmanned vehicles under emergency conditions is created for a manned vehicle that is about to pass through an intersection, for example, the unmanned vehicle will have to stop away from the stop line drawn just before the intersection, disrupting traffic flow.

[0008] On the other hand, even after a manned vehicle has passed through an intersection, the intersection remains within the emergency no-entry zone for unmanned vehicles for a while, meaning that the unmanned vehicle cannot immediately depart. In other words, the unmanned vehicle continues to be stopped just before the intersection, disrupting traffic flow in this case as well.

[0009] The present invention has been made in consideration of the problems described above, and aims to provide a control device and a control system that can realize a safe traffic flow that suppresses interference between moving bodies in an interference area, even if the state regarding the movement of the moving bodies differs from the predicted state. [Means for solving the problem]

[0010] The control device according to claim 1 includes a receiving unit that receives mobile body information representing the state of each mobile body including an autonomous mobile body that receives constraint conditions that specify a travel time and space, an abnormality detection unit that detects whether or not an abnormality has occurred in the movement of an approaching mobile body from a change in state of an approaching mobile body that may approach the autonomous mobile body within a predetermined range in an interference area among each mobile body that receives mobile body information by the receiving unit, and, when an abnormality is detected by the abnormality detection unit, detects whether or not an abnormality has occurred in the movement of the approaching mobile body from a change in state of the approaching mobile body that may approach the autonomous mobile body within a predetermined range in an interference area. The entry prohibition time for prohibiting entry into the interference area is lengthened, The autonomous moving body is provided with a generating unit that generates, for the autonomous moving body, a strengthened constraint condition, which is a constraint condition that strengthens the constraint so that the autonomous moving body is less likely to interfere with the approaching moving body in the interference region.

[0011] In the invention described in claim 2, the abnormality detection unit detects that an abnormality regarding the movement of the approaching moving body has occurred when a status value regarding the state of the approaching moving body is not included within a prediction range set using the moving body information of the approaching moving body received by the receiving unit.

[0012] In the invention described in claim 3, the abnormality detection unit detects that an abnormality has occurred in the movement of the approaching moving body when the state value is not included within the prediction range set for at least one of the states of the approaching moving body: the speed of the approaching moving body, the acceleration of the approaching moving body, the passing time of the approaching moving body through the interference area, and the position of the approaching moving body.

[0013] In the invention described in claim 4, the abnormality detection unit detects that a movement abnormality has occurred in the approaching moving body when the measurement accuracy of the state value regarding at least one of the states of the approaching moving body, i.e., the speed of the approaching moving body, the acceleration of the approaching moving body, the passing time of the approaching moving body through the interference area, and the position of the approaching moving body, is lower than a predetermined standard accuracy, even if the state value is included in the prediction range.

[0015] Claim5 In the described invention, the generation unit adjusts the extension time of the no-entry time into the interference area in the reinforced constraint condition, which is extended relative to the no-entry time into the interference area in the constraint condition generated before the abnormality was detected by the anomaly detection unit, using the shape of the interference area.

[0016] Claim 6 In the described invention, when the approaching moving body is a connected moving body that receives the constraint conditions generated by the generation unit and moves in accordance with the received constraint conditions, the generation unit generates the reinforced constraint conditions for the autonomous moving body and the connected moving body, respectively, in which the no-entry times for entry into the interference area for each of the autonomous moving body and the connected moving body are set so as to overlap.

[0017] Claim 7 In the described invention, as the number of times abnormalities regarding the movement of the approaching moving body are detected increases, the generation unit generates for the autonomous moving body reinforced constraint conditions in which the no-entry time for entering the interference area is set longer than the constraint conditions generated before the abnormality was detected by the anomaly detection unit.

[0018] Claim 8 In the described invention, the generation unit generates reinforced constraint conditions for the autonomous moving body that are set so that the range in which entry of the autonomous moving body is prohibited is wider than the constraint conditions generated before the abnormality was detected by the anomaly detection unit.

[0019] Claim 9 In the described invention, as the number of times abnormalities regarding the movement of the approaching moving body are detected increases, the generation unit generates enhanced constraint conditions for the autonomous moving body in which the range of the interference area that prohibits the entry of the autonomous moving body is set wider than the constraint conditions generated before the abnormality was detected by the anomaly detection unit.

[0020] Claim 10The described control system includes an acquisition device that acquires, in an interference area, moving body information representing the state of an approaching moving body that may approach within a predetermined range of an autonomous moving body that receives constraint conditions specifying a movement time and space, and the moving body information representing the state of the autonomous moving body, a receiving unit that receives from the acquisition device the moving body information of the autonomous moving body and the approaching moving body acquired by the acquisition device, an anomaly detection unit that detects whether or not an abnormality has occurred regarding the movement of the approaching moving body from a change in the state of the approaching moving body represented by the moving body information received by the receiving unit, and, when an abnormality is detected by the anomaly detection unit, detects whether or not an abnormality has occurred regarding the movement of the approaching moving body from a change in the state of the approaching moving body represented by the moving body information received by the receiving unit, and, The entry prohibition time for prohibiting entry into the interference area is lengthened, The autonomous moving body is provided with a generating unit that generates, for the autonomous moving body, a strengthened constraint condition, which is a constraint condition that strengthens the constraint so that the autonomous moving body is less likely to interfere with the approaching moving body in the interference region. Effect of the Invention

[0021] According to the present invention, even if the state of the movement of the mobile bodies differs from the predicted state, it is possible to realize a safe traffic flow that suppresses interference between mobile bodies in the interference area. [Brief description of the drawings]

[0022] [Figure 1] FIG. 2 is a diagram illustrating an example of a system configuration of a control system. [Diagram 2] FIG. 2 is a diagram illustrating an example of a functional configuration of a control device of a compliant vehicle. [Diagram 3] FIG. 2 is a diagram illustrating an example of a functional configuration of a control device for a semi-compliant vehicle. [Figure 4] FIG. 2 is a diagram illustrating an example of a functional configuration of a control device of a non-compliant vehicle. [Diagram 5] FIG. 1 is a diagram showing an example of differences in driving functions for each vehicle type. [Figure 6] FIG. 2 is a diagram illustrating an example of a functional configuration of a collection device and a control device. [Figure 7] FIG. 13 is a diagram showing an example of the contents of a controlled driving route map. [Figure 8] FIG. 13 is a diagram showing an example of an interference point; [Figure 9] FIG. 11 is a diagram illustrating an example of interference point information. [Figure 10] FIG. 1 illustrates an example of a global route. [Figure 11] FIG. 2 is a diagram showing an example of a travel route. [Figure 12] FIG. 2 is a diagram showing an example of the configuration of a main part of an electrical system in a control device. [Figure 13] FIG. 2 is a diagram illustrating an example of the configuration of a main part of an electrical system in a vehicle control device. [Figure 14] 4 is a flowchart showing an example of the flow of a control process executed by a control device. [Figure 15] FIG. 13 is a diagram showing an example of an interference time of a vehicle at an interference point. [Figure 16] FIG. 13 is a schematic diagram illustrating a virtual traffic rule at an interference point where state sx=0 is set. [Figure 17] FIG. 13 is a schematic diagram illustrating a virtual traffic rule at an interference point set to state sx=1. [Figure 18] FIG. 13 is a schematic diagram illustrating coordination of interference points. [Figure 19] 13 is a flowchart showing an example of the flow of a virtual traffic rule generation process. [Figure 20] FIG. 1 is a diagram showing an example of traffic flow caused by changes in vehicle speed. [Figure 21] FIG. 13 is a diagram showing an example of virtual traffic rules for an automatic vehicle in response to a speed change of a manual vehicle. [Figure 22] FIG. 13 illustrates an example of a situation where the vehicle speed is not within a predicted range. [Diagram 23] FIG. 13 is a diagram illustrating an example of an enhanced virtual traffic rule. [Figure 24] FIG. 13 is a diagram showing an example of a state in which the vehicle speed is within a predicted range. [Diagram 25] FIG. 13 is a diagram showing an example of a no-entry area. [Figure 26] FIG. 13 is a diagram showing an example of the enhanced virtual traffic rules in a situation where the passing priority order is set to priority. [Figure 27]FIG. 13 is a diagram showing an example of a constraint condition when vehicles approach each other in a situation where the passing priority order is set to priority; [Figure 28] FIG. 13 is a diagram showing an example of the enhanced virtual traffic rules in a situation where the passing priority order is set to non-priority. [Figure 29] FIG. 13 is a diagram illustrating an example of a constraint condition when vehicles approach each other in a situation where the passing priority order is set to non-priority. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Hereinafter, an embodiment will be described with reference to the drawings. Note that the same components and processes are given the same reference numerals throughout the drawings, and duplicated explanations will be omitted.

[0024] 1 is a diagram showing an example of the system configuration of a control system 1 according to this embodiment. The control system 1 includes a vehicle 7 to be controlled by the control system 1, a plurality of wireless communication devices 3 and collection devices 9 each installed along a route 6, and a control device 10. Each of the wireless communication devices 3 and collection devices 9 is connected to the control device 10 via a communication network 5.

[0025] Vehicles 7 are classified into a plurality of types according to differences in driving functions, which are functions related to driving, that are provided in the vehicles 7. For example, among the vehicles 7, a vehicle 7 that is provided with wireless equipment and wirelessly connects to, for example, any one of the wireless communication devices 3 installed in the nearest location while traveling to perform data communication with the control device 10 through the communication network 5 is classified as a connected vehicle, and a vehicle 7 that is not provided with wireless equipment and cannot perform data communication with the control device 10 is classified as a non-connected vehicle.

[0026] The wireless communication device 3 serves as a data relay device between the connected vehicle and the control device 10. There are no restrictions on where the wireless communication device 3 can be installed as long as it is within a range where wireless communication with the connected vehicle is possible, but it is preferable to install it as close to the road as possible, such as on a sidewalk running parallel to the road, a median strip of the road, or a traffic light. There are also no restrictions on the number of wireless communication devices 3 that can be installed.

[0027] The communication network 5 transmits various information about the vehicles 7 collected by the wireless communication devices 3 and the collection devices 9 to the control device 10, and also transmits control information about the connected vehicles generated by the control device 10 to the wireless communication devices 3 designated by the control device 10. The control information is transmitted to all wireless communication devices 3 by instruction from the control device 10, and may also be notified to each connected vehicle. The communication network 5 may be a wired line or a wireless line.

[0028] The control device 10 is a device that performs traffic control to avoid interference at a location where the vehicles 7 approach each other within a predetermined range (hereinafter referred to as the "interference range") where there is a possibility of collision between the vehicles 7, that is, at the interference point X, without reducing the traffic efficiency between the vehicles 7. Here, "interference" refers not only to a situation where the vehicles 7 come into contact with each other, but also to a situation where the vehicles 7 approach each other within the interference range.

[0029] The control device 10 receives moving object information indicating the state of the vehicles 7 from each vehicle 7 to be controlled, generates constraint conditions (hereinafter referred to as "virtual traffic rules") that constrain the decisions regarding the movement made by each vehicle 7 for each vehicle 7 having a function for receiving the constraint conditions, and transmits them to each vehicle 7 having a function for receiving the constraint conditions. In this way, traffic control is performed so that the vehicles 7 do not interfere with each other at the interference point X.

[0030] Here, the "status of vehicle 7" refers to a collection of information that represents items related to vehicle 7 that can be measured and collected, such as the movement of vehicle 7, the control performed on vehicle 7, and the operating status of each function equipped on vehicle 7.

[0031] The virtual traffic rules transmitted by the control device 10 to the vehicle 7 are an example of constraints on the vehicle 7, and the details will be explained later.

[0032] If the vehicle is connected, the control device 10 can receive moving object information through the wireless communication device 3, but if the vehicle is non-connected, the control device 10 cannot receive moving object information through the wireless communication device 3 because the vehicle does not have wireless equipment.

[0033] Therefore, the control device 10 acquires moving object information of non-connected vehicles through the collection device 9.

[0034] The collection device 9 is a device that uses, for example, an optical imaging unit or a UWB (Ultra Wide Band) radar to photograph or measure non-connected vehicles in lane 8 and the traffic environment around the non-connected vehicles, and collects moving object information regarding the non-connected vehicles.

[0035] Connected vehicles are divided into three types based on differences in driving functions: compliant vehicles, semi-compliant vehicles, and non-compliant vehicles.

[0036] A compliant vehicle is a vehicle 7 equipped with an automatic driving function that receives virtual traffic rules from the control device 10, and drives while controlling the vehicle 7 at its own discretion so as to satisfy the received virtual traffic rules while referring to traffic environment information obtained from sensors or the like attached to the vehicle 7 and autonomously avoid interference between the vehicles 7. Note that the automatic driving according to this embodiment refers to driving classified as level 3 or higher, in which the automatic driving function, not the driver, is responsible for driving. A compliant vehicle is an example of an autonomous moving body that moves at the vehicle 7's own discretion.

[0037] A semi-compliant vehicle is a vehicle 7 that receives virtual traffic rules from the control device 10, but unlike a compliant vehicle, the vehicle 7 does not control its driving so that it itself satisfies the virtual traffic rules. Instead, the driver who receives the virtual traffic rules controls the steering wheel and accelerator at his or her own discretion so as to satisfy the virtual traffic rules and avoid interference between other vehicles 7.

[0038] A non-compliant vehicle is a vehicle 7 that can transmit mobile information to the control device 10 through the wireless communication device 3 but cannot receive virtual traffic rules from the control device 10.

[0039] Hereinafter, when there is no need to distinguish between compliant vehicles, semi-compliant vehicles, non-compliant vehicles, and non-connected vehicles, they will be collectively referred to as "vehicles 7", and when there is a need to distinguish between them, they will be referred to as compliant vehicles 7A, semi-compliant vehicles 7B, non-compliant vehicles 7C, and non-connected vehicles 7D, respectively.

[0040] Semi-compliant vehicle 7B, non-compliant vehicle 7C, and non-connected vehicle 7D are sometimes referred to as "manual vehicles" because the vehicle 7 is driven by the driver's operation. On the other hand, compliant vehicle 7A is sometimes referred to as an "automatic vehicle" in contrast to manual vehicles because the vehicle 7 is driven by its own judgment and control.

[0041] The degree to which the vehicle 7 moves along the route 6 instructed by the control device 10 through control information, or the degree to which the vehicle 7 moves along the route 6 predicted by the control device 10, is called the "degree of compliance," and the higher the degree of compliance, the higher the probability that the vehicle 7 will move along the route 6 instructed by the control device 10 or the route 6 predicted by the control device 10. Note that, since the movement of the vehicle 7 is restricted by virtual traffic rules, the degree of compliance is sometimes called the degree of compliance with virtual traffic rules, or the degree of compliance regarding movement.

[0042] Among the vehicles 7, compliant vehicles 7A have the highest level of compliance, followed by semi-compliant vehicles 7B. The level of compliance of non-compliant vehicles 7C is lower than semi-compliant vehicles 7B, and the level of compliance of non-connected vehicles 7D is the lowest among the vehicles 7. In other words, the types of vehicles 7 according to this embodiment are classified according to the level of compliance of each vehicle 7.

[0043] FIG. 2 is a diagram showing an example of a functional configuration of the control device of the compliant vehicle 7A.

[0044] As shown in FIG. 2, the control device of the compliant vehicle 7A includes functional units such as 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 travel route map .

[0045] The position estimation unit 21A estimates the position of the compliant vehicle 7A. Specifically, the position estimation unit 21A acquires external shape data from an external sensor, such as a laser range finder or a camera attached to the compliant vehicle 7A, that captures the shape of the surrounding environment in which the compliant vehicle 7A runs and the situation on the lane 8, which is an example of the moving path of the compliant vehicle 7A, which is a moving body, and estimates the position of the compliant vehicle 7A by matching the acquired external shape data with, for example, environmental external shape data (a map for self-position estimation) prepared in advance and estimating the matching point as the current position of the compliant vehicle 7A. There is no restriction on the method of estimating the position of the compliant vehicle 7A in the position estimation unit 21A, and other estimation methods, such as estimating the position of the compliant vehicle 7A using a GPS (Global Positioning System), may be used. The position estimation unit 21A notifies the state management unit 22A and the local route planning unit 24 of the estimated position of the compliant vehicle 7A as position information. Such a position estimation unit 21A corresponds to a first position estimation unit according to this embodiment.

[0046] The state management unit 22A manages vehicle-specific information including information used to uniquely identify the compliant vehicle 7A, such as license plate information and vehicle number. The state management unit 22A also collects, in chronological order, sensor values ​​relating to the surrounding traffic environment acquired by sensors (called "internal sensors") and external sensors that measure the position of the vehicle 7 represented by position information, as well as posture, speed, and control details, and manages them as the state of the compliant vehicle 7A.

[0047] 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 compliant vehicle 7A as moving body information at least periodically and / or whenever necessary.

[0048] The wireless communication unit 23A transmits the moving object information received from the status management unit 22A to the control device 10 via the wireless communication device 3, and notifies the local route planning unit 24 of the control information received from the control device 10 via the wireless communication device 3 without delay.

[0049] The local route planning unit 24 plans a local route using the position information received from the position estimation unit 21A, the moving object information received from the state management unit 22A, the control information received from the wireless communication unit 23A, and the vehicle driving route map 26, and notifies the control unit 25A.

[0050] The local route planning unit 24 determines the state of the roadway on which the compliant vehicle 7A is traveling from the measurement data of the external sensor attached to the compliant vehicle 7A included in the moving body information, under the condition that the compliant vehicle 7A satisfies the virtual traffic rules included in the traffic control information and travels along the global route specified in the traffic control information, and determines the actual route, such as the position on the roadway corresponding to the global route along which the compliant vehicle 7A must actually travel. Then, the local route planning unit 24 sets the speed and attitude of the compliant vehicle 7A at each time to be followed in order for the vehicle 7 to travel along the determined actual route.

[0051] In this way, the actual route determined under the condition that the compliant vehicle 7A travels along the global route specified by the control device 10 while satisfying the virtual traffic rules is called a "local route", and the local route is added with control content for making the compliant vehicle 7A travel along the local route. The details of the global route will be explained later.

[0052] The vehicle driving route map 26 includes map information showing the lanes 8 on which the compliant vehicle 7A is to drive, and is used to determine local routes and to set the control contents of the compliant vehicle 7A at each time so as to drive the compliant vehicle 7A along the local routes.

[0053] When the control unit 25A receives a local route from the local route planning unit 24, it controls the steering wheel, accelerator, brakes, etc. of the compliant vehicle 7A in accordance with the control content included in the local route, thereby realizing autonomous driving of the compliant vehicle 7A along the local route.

[0054] The control contents of the compliant vehicle 7A, such as the operation amounts of the steering wheel, accelerator, brake, etc. associated with the control performed by the control unit 25A, are notified to the status management unit 22A via each internal sensor that measures various control amounts, and are managed by the status management unit 22A as the status of the compliant vehicle 7A.

[0055] Incidentally, even if the compliant vehicle 7A does not receive a global route from the control device 10, it can determine the condition of the road on which it is traveling from the measurement data of the external sensors and determine a real route that satisfies the virtual traffic rules. However, as an example here, it is assumed that the compliant vehicle 7A receives a global route from the control device 10 and determines a real route, i.e., a local route.

[0056] FIG. 3 is a diagram showing an example of a functional configuration of a control device of the semi-compliant vehicle 7B.

[0057] As shown in FIG. 3, the control device of the semi-compliant vehicle 7B includes a position estimation unit 21B, a state management unit 22B, a wireless communication unit 23B, a control unit 25B, and a virtual traffic rule transmission unit 27.

[0058] Since the driver of the semi-compliant vehicle 7B determines the actual route, the semi-compliant vehicle 7B does not have a local route planning unit 24 and a vehicle driving route map 26 as in the compliant vehicle 7A, and instead has a virtual traffic rule transmission unit 27.

[0059] The position estimation unit 21B estimates the position of the semi-compliant vehicle 7B in the same manner as the position estimation unit 21A in the compliant vehicle 7A, and notifies the status management unit 22B of the estimated position of the semi-compliant vehicle 7B as position information.

[0060] The status management unit 22B collects and manages the mobile object information of the semi-compliant vehicle 7B, similar to the status management unit 22A in the compliant vehicle 7A. In the case of the semi-compliant vehicle 7B, since the driver controls the semi-compliant vehicle 7B, the status of the semi-compliant vehicle 7B includes the amount of operation of the steering wheel, accelerator, brake, etc. operated by the driver.

[0061] Moreover, the state management unit 22B notifies the managed mobile object information to the wireless communication unit 23B at least one of periodically and whenever necessary.

[0062] The wireless communication unit 23B transmits the moving object information received from the status management unit 22B to the control device 10 via the wireless communication device 3, and notifies the virtual traffic rule transmission unit 27 of the control information received from the control device 10 via the wireless communication device 3 without delay.

[0063] The virtual traffic rule transmission unit 27 transmits the virtual traffic rules included in the control information received from the control device 10 to the driver through the wireless communication unit 23B. There are no restrictions on the method of transmitting the virtual traffic rules in the virtual traffic rule transmission unit 27 as long as the driver of the semi-compliant vehicle 7B can understand the virtual traffic rules, and any of the following methods may be used: display, audio, and tactile transmission. Also, the virtual traffic rules may be transmitted to the driver by a combination of a plurality of transmission methods.

[0064] In addition, the semi-compliant vehicle 7B may receive a global route from the control device 10 and transmit the global route together with the virtual traffic rules to the driver via the virtual traffic rule transmission unit 27 to provide driving assistance to the driver, but as an example, the following explanation will be given assuming that the semi-compliant vehicle 7B does not receive a global route from the control device 10.

[0065] FIG. 4 is a diagram showing an example of a functional configuration of the control device of the non-compliant vehicle 7C.

[0066] As shown in FIG. 4, the control device of a non-compliant vehicle 7C includes a position estimation unit 21C, a state management unit 22C, a wireless communication unit 23C, and a control unit 25C.

[0067] Since the non-compliant vehicle 7C is unable to receive virtual traffic rules from the control device 10, or does not have the function to utilize the virtual traffic rules even if it receives the virtual traffic rules, the virtual traffic rule transmission unit 27 is removed from the semi-compliant vehicle 7B.

[0068] The position estimation unit 21C estimates the position of the non-compliant vehicle 7C in the same manner as the position estimation unit 21A in the compliant vehicle 7A, and notifies the state management unit 22C of the estimated position of the non-compliant vehicle 7C as position information.

[0069] The status management unit 22C collects and manages the mobile object information of the non-compliant vehicle 7C, similar to the status management unit 22B in the semi-compliant vehicle 7B. The status management unit 22C also notifies the wireless communication unit 23C of the managed mobile object information at least periodically and / or whenever necessary.

[0070] The wireless communication unit 23C transmits the moving object information received from the state management unit 22C to the control device 10 via the wireless communication device 3.

[0071] In addition, the non-compliant vehicle 7C may receive a global route from the control device 10 and transmit the received global route to the driver to provide driving assistance to the driver, but as an example, the non-compliant vehicle 7C will be described as not receiving a global route from the control device 10.

[0072] The non-compliant vehicle 7C may have some kind of automatic driving function if it is unable to receive virtual traffic rules from the control device 10, or if it receives the virtual traffic rules but does not have the function to utilize the virtual traffic rules, but as an example here, it is assumed that the driver is manually driving the non-compliant vehicle 7C.

[0073] FIG. 5 is a diagram summarizing the differences in driving functions possessed by the above-mentioned compliant vehicle 7A, semi-compliant vehicle 7B, non-compliant vehicle 7C, and non-connected vehicle 7D.

[0074] In Figure 5, "◯" indicates that the corresponding operation function is provided, and "×" indicates that the corresponding operation function is not provided. "◯ / ×" indicates that it does not matter whether the corresponding operation function is provided or not, and "-" indicates that the operation function is not required in principle.

[0075] Hereinafter, for ease of explanation, when it is necessary to distinguish between different types of mobile information, the mobile information of compliant vehicle 7A will be referred to as "mobile information A," the mobile information of semi-compliant vehicle 7B will be referred to as "mobile information B," and the mobile information of non-compliant vehicle 7C will be referred to as "mobile information C."

[0076] On the other hand, Fig. 6 is a diagram showing an example of the functional configuration of the collecting device 9 and the control device 10. As shown in Fig. 6, the collecting device 9 includes an infrastructure sensor 9A and a state detecting unit 9B.

[0077] The infrastructure sensor 9A is, for example, a photographing unit or UWB radar that photographs optically, and photographs or measures the non-connected vehicle 7D and the traffic environment around the non-connected vehicle 7D. The infrastructure sensor 9A notifies the state detection unit 9B of collected data on the running state and traffic environment of the non-connected vehicle 7D that has been photographed or measured. The infrastructure sensor 9A photographs or measures the traffic environment at predetermined intervals, for example, but may photograph or measure the traffic environment only for a certain period after detecting a moving body such as the non-connected vehicle 7D. As an example, the infrastructure sensor 9A is a camera that constantly photographs images at predetermined intervals. If the image photographing interval is set to be short, the image photographed by the infrastructure sensor 9A becomes a video. Such an infrastructure sensor 9A corresponds to an acquisition device that acquires the state of the vehicle 7.

[0078] When the state detection unit 9B receives the collected data from the infrastructure sensor 9A, it extracts the vehicle portion of the non-connected vehicle 7D from the image captured by the infrastructure sensor 9A by known image processing, and performs coordinate conversion of the position of the non-connected vehicle 7D in the image from the camera coordinate position in the infrastructure sensor 9A to the map coordinate position representing the shape of the actual road, thereby calculating the position of the non-connected vehicle 7D on the controlled driving route map 19. If the position of the non-connected vehicle 7D on the controlled driving route map 19 is determined, the speed of the non-connected vehicle 7D can be obtained from, for example, the positional deviation of the non-connected vehicle 7D obtained from two images and the time difference when the two images were captured. In addition, it is possible to track how the non-connected vehicle 7D is moving from the change in the position of the non-connected vehicle 7D over time.

[0079] In this way, the state detection unit 9B detects moving body information of the non-connected vehicle 7D, such as the position, attitude, and speed of the non-connected vehicle 7D, and transmits the moving body information to the control device 10 without delay via the communication network 5. Hereinafter, the moving body information of the non-connected vehicle 7D may be referred to as "moving body information D."

[0080] The collecting device 9 does not necessarily have to include the infrastructure sensor 9A and the status detection unit 9B, and may include only the infrastructure sensor 9A, for example. In this case, a status detection device (not shown) including the status detection unit 9B is connected to the communication network 5, and the status detection device (not shown) receives the collected data notified from each collecting device 9 to detect the moving body information of the unconnected vehicle 7D. Since the status detection unit 9B is not required from each collecting device 9, the collecting device 9 can be made smaller than if the collecting device 9 included the status detection unit 9B, and the collecting device 9 can be easily installed.

[0081] Like the wireless communication device 3, the collection device 9 is also installed in an area where moving object information of the non-connected vehicle 7D can be obtained, such as a sidewalk running parallel to the roadway, a median strip of the roadway, and a traffic light facility. The shooting direction of the infrastructure sensor 9A in the collection device 9 and the range (angle of view) of the captured image may be fixed or variable.

[0082] Next, the function of the control device 10 will be described.

[0083] As shown in FIG. 6, the control device 10 includes functional units, such as 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, an anomaly detection unit 16, and a virtual traffic rule generation unit 18, as well as a controlled driving route map 19.

[0084] The communication unit 12 receives mobile object information from each of the compliant vehicles 7A, semi-compliant vehicles 7B, and non-compliant vehicles 7C via the wireless communication device 3, and transmits control information including virtual traffic rules generated by a virtual traffic rule generation unit 18 described later to each of the compliant vehicles 7A and semi-compliant vehicles 7B designated as destinations of the control information. The communication unit 12 is an example of a receiving unit that receives mobile object information.

[0085] Since the compliant vehicle 7A is a vehicle 7 that travels along a global route designated by the control device 10, the control device 10 needs to plan a global route.

[0086] Therefore, the destination setting unit 11 receives the destination to which the compliant vehicle 7A is heading and the vehicle-specific information of the compliant vehicle 7A, and notifies the global route planning unit 13 of the correspondence between the vehicle-specific information of the compliant vehicle 7A and the destination.

[0087] The destination and vehicle-specific information of the compliant vehicle 7A may be set in the destination setting unit 11 by the operator of the control device 10, or mobile object information A including the destination may be received from the compliant vehicle 7A, and the destination setting unit 11 may associate the vehicle-specific information contained in the received mobile object information A with the destination and notify the global route planning unit 13.

[0088] The global route planning unit 13 plans a global route using the destination of the compliant vehicle 7A received from the destination setting unit 11, the moving object information A of the compliant vehicle 7A received from the communication unit 12, and the controlled driving route map 19. The global route planning unit 13 notifies the interference point identification unit 15 of the planned global route.

[0089] 7 is a diagram showing an example of the contents of the controlled driving route map 19 used for planning global routes. The controlled driving route map 19 is a road structure database expressed by a collection of route information for each section, with the section where traffic regulations do not change being the smallest unit.

[0090] Each section that makes up the controlled driving route map 19 (in the example of Figure 7, six sections: the section from point K1 to point K2, the section from point K2 to point K3, the section from point K3 to point K7, 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) is set with traffic regulation information such as the speed limit, number of lanes, curvature, width, gradient, and lane priority, i.e. whether a lane is a priority lane or a non-priority lane when lanes in the section intersect.

[0091] Furthermore, each section constituting the controlled driving route map 19 represents the route 6 of the vehicle 7 by a sequence of waypoints, which is a collection of virtually set waypoints. A waypoint is one of the indicators representing the position of the vehicle 7 on the route 6, and since a unique waypoint ID is set for each waypoint, the position of the vehicle 7 on the route 6 can be identified from the waypoint ID.

[0092] The controlled driving route map 19 includes interference point information that predefines the interference point X.

[0093] FIG. 8 is a diagram showing an example of an interference point X. For example, interference point X includes an intersection where a priority lane 8A and a non-priority lane 8B intersect as shown in FIG. 8(A), and a junction where a priority lane 8A and a non-priority lane 8B merge as seen on a general road or expressway as shown in FIG. 8(B). Since interference point X is a location where vehicles 7 approach each other to the extent that they fall within the interference range, interference point X is not necessarily represented by a point, and may be represented by an area having a certain size. In other words, interference point X is an example of an interference area.

[0094] FIG. 9 is a diagram showing an example of interference point information. The interference point information is information that specifies, for each interference point X, an interference point ID for uniquely identifying the interference point X, the position of the interference point X, a waypoint ID for uniquely identifying the waypoint corresponding to the interference point X, and the position and waypoint ID of the waypoint immediately preceding the interference point X, for each of the priority lane 8A and the non-priority lane 8B that share the same interference point X.

[0095] The vehicle driving route map 26 for the aforementioned compliant vehicle 7A is also composed of the same information as the controlled driving route map 19, but the vehicle driving route map 26 may also include information that is necessary for the compliant vehicle 7A to actually drive and that is not included in the controlled driving route map 19.

[0096] It should be noted that the controlled driving route map 19 does not necessarily need to include interference point information, and in this case, the control device 10 may generate interference point information from the information of the sequence of waypoints included in the controlled driving route map 19. Also, the controlled driving route map 19 does not necessarily need to be included in the control device 10, and the control device 10 may obtain the controlled driving route map 19 from an external device different from the control device 10 via the communication unit 12.

[0097] 6 searches for a global route, which is a route 6 from the current position of the compliant vehicle 7A to the destination on the controlled driving route map 19, for each compliant vehicle 7A, based on the vehicle-specific information and destination of the compliant vehicle 7A received from the destination setting unit 11, the position information and vehicle-specific information of the compliant vehicle 7A included in the mobile unit information A, and the controlled driving route map 19. In other words, the global route is expressed as a route 6 connecting a series of waypoints of each section that constitutes the controlled driving route map 19.

[0098] 10 is a diagram showing an example of global routes of vehicle P and vehicle Q, both of which are compliant vehicles 7A. This shows a situation in which one route 6P connecting the current position of vehicle P and the destination of vehicle P is selected as the global route of vehicle P from among multiple routes 6, and one route 6Q connecting the current position of vehicle Q and the destination of vehicle Q is selected as the global route of vehicle Q.

[0099] Any search method may be used in the global route planning unit 13 to search for the global route of the compliant vehicle 7A, and a known search method such as Dijkstra search may be used, for example.

[0100] Since the destination of the compliant vehicle 7A is determined in advance, the control device 10 can take the initiative in planning the global route of the compliant vehicle 7A in the global route planning unit 13. Since the compliant vehicle 7A travels along a local route determined based on the global route, the control device 10 can know the route 6 of the compliant vehicle 7A from the global route that it has planned.

[0101] On the other hand, in the case of semi-compliant vehicle 7B, the driver determines the route 6, so the destination may not be predetermined. Also, if a driver is driving non-compliant vehicle 7C, the driver determines the route 6, just like semi-compliant vehicle 7B, so the destination may not be predetermined. Furthermore, in the case of non-connected vehicle 7D, data communication with control device 10 is not possible, so control device 10 has no means for acquiring the destination to which non-connected vehicle 7D is heading in the first place.

[0102] Therefore, the control device 10 cannot plan global routes for the semi-compliant vehicle 7B, the non-compliant vehicle 7C, and the non-connected vehicle 7D in the global route planning unit 13.

[0103] Instead, the control device 10 predicts the driving routes of the semi-compliant vehicle 7B, the non-compliant vehicle 7C, and the non-connected vehicle 7D using the driving route prediction unit 14.

[0104] Specifically, the travel route prediction unit 14 predicts the travel route of the semi-compliant vehicle 7B, the non-compliant vehicle 7C, and the non-connected vehicle 7D, i.e., the manually-operated vehicle, from the position information and speed included in the moving body information B, the moving body information C, and the moving body information D. For example, the travel route prediction unit 14 assumes that the manually-operated vehicle travels a specific distance (e.g., 100 m) from the current position at the speed indicated in the moving body information, and notifies the interference point identification unit 15 of the route 6 connecting the via points of the section of the lane 8 corresponding to the specific distance as the travel route of the manually-operated vehicle.

[0105] However, since the destination of the manual vehicle is unknown, the driving route prediction unit 14 treats the combination of all routes 6 that branch out each time a lane 8 branches as the driving route, unlike a global route, when a lane 8 branches.

[0106] Fig. 11 is a diagram showing an example of a travel route when the vehicle P is a manual vehicle. When the travel destination of the vehicle P branches into two as shown in Fig. 11, the travel route prediction unit 14 determines both the route 6P-1 and the route 6P-2 as the travel route of the vehicle P because it is unknown in which direction the vehicle P will branch at the branch point.

[0107] As already explained, the mobile object information B of the semi-compliant vehicle 7B and the mobile object information C of the non-compliant vehicle 7C are obtained through the wireless communication device 3, and the mobile object information D of the non-connected vehicle 7D is obtained through the collection device 9.

[0108] The images captured by the collecting device 9 may include vehicles 7 of types other than the non-connected vehicle 7D. In this case, it may be difficult to distinguish which of the moving body information acquired from the collecting device 9 is the moving body information D of the non-connected vehicle 7D. Therefore, when the vehicle 7 is approaching within a predetermined range where the position information included in the moving body information A, moving body information B, and moving body information C acquired through the communication unit 12 is deemed to match the position information included in the moving body information of the vehicle 7 acquired from the collecting device 9, the travel path predicting unit 14 may determine that the vehicle 7 represented by the moving body information notified from the collecting device 9 is any of the compliant vehicle 7A, semi-compliant vehicle 7B, and non-compliant vehicle 7C represented by the moving body information acquired from the communication unit 12, and distinguish it from the moving body information D of the non-connected vehicle 7D.

[0109] The interference point identification unit 15 identifies an interference point X on each route 6 based on the global route of the compliant vehicle 7A received from the global route planning unit 13 and the travel routes of the semi-compliant vehicle 7B, the non-compliant vehicle 7C, and the non-connected vehicle 7D predicted by the travel route prediction unit 14. In addition, the interference point identification unit 15 notifies the virtual traffic rule generation unit 18 of the route 6 of each vehicle 7 and the interference point X on each route 6.

[0110] The abnormality detection unit 16 receives moving body information of each vehicle 7 from the communication unit 12 and the collection device 9, and detects whether or not a movement-related abnormality has occurred in the approaching vehicle from a change in the state related to the movement of the approaching vehicle represented by the received moving body information.

[0111] Here, the “approaching vehicle” refers to a vehicle 7 that, among the vehicles 7 that have received moving object information about the specific vehicle 7, may approach within a predetermined range of the specific vehicle 7 at the interference point X. In addition, the “abnormality regarding the movement” of the approaching vehicle refers to an unexpected movement of the approaching vehicle that is not predicted by the control device 10.

[0112] If it is found that an abnormality in movement has occurred in a vehicle 7 approaching a specific vehicle 7, the control device 10 can suppress interference between the specific vehicle 7 and the approaching vehicle at the interference point X by transmitting to the specific vehicle 7 virtual traffic rules with stronger restrictions on movement so as not to interfere with the approaching vehicle 7 at the interference point X.

[0113] However, when the specific vehicle 7 is a manual vehicle, the driver does not necessarily drive the manual vehicle according to the virtual traffic rules, and in particular, when the specific vehicle 7 is a non-connected vehicle 7D, the driver cannot receive the virtual traffic rules. That is, it is preferable that the specific vehicle 7 is a compliant vehicle 7A that is driven according to the virtual traffic rules. Therefore, the abnormality detection unit 16 according to the present embodiment recognizes a manual vehicle approaching the compliant vehicle 7A as an approaching vehicle, and detects whether an abnormality has occurred regarding the movement of the manual vehicle.

[0114] The abnormality detection unit 16 notifies the virtual traffic rule generation unit 18 of the determination result indicating whether or not an abnormality has occurred regarding the movement of the approaching vehicle.

[0115] In the following, an abnormality regarding the movement of an approaching vehicle will simply be referred to as an "abnormality" of the approaching vehicle. A method for detecting an abnormality in an approaching vehicle will be described in detail later. In FIG. 6, in order to clearly show the destination of notification of the moving object information D from the collecting device 9, the moving object information D is notified from the collecting device 9 to the travel route predicting unit 14 and the abnormality detecting unit 16, but in reality, the moving object information D is notified to the travel route predicting unit 14 and the abnormality detecting unit 16 via the communication unit 12.

[0116] The virtual traffic rule generation unit 18 generates virtual traffic rules for the compliant vehicles 7A and the semi-compliant vehicles 7B, using the moving object information of each vehicle 7 received from the communication unit 12, the route 6 of each vehicle 7 received from the interference point identification unit 15, i.e., the global route and the travel route, and the interference point X on each route 6, and the determination result regarding an abnormality of the approaching vehicle received from the abnormality detection unit 16. The virtual traffic rule generation unit 18 corresponds to the generation unit according to this embodiment.

[0117] Specifically, the virtual traffic rule generation unit 18 first uses the moving object information of each vehicle 7, the route 6 of each vehicle 7, and the interference point X on each route 6 to estimate the interference time t at each interference point X for the compliant vehicle 7A and other vehicles 7 that interfere with each other at the interference point X on the global route of the compliant vehicle 7A, and specifies a passing priority for the compliant vehicle 7A such that interference between the vehicles 7 at each interference point X is avoided.

[0118] Similarly, the virtual traffic rule generation unit 18 uses the moving object information of each vehicle 7, the route 6 of each vehicle 7, and the interference point X on each route 6 to estimate the interference time t at each interference point X for the semi-compliant vehicle 7B and other vehicles 7 that interfere with each other at the interference point X on the driving route of the semi-compliant vehicle 7B, and specifies a passing priority for the semi-compliant vehicle 7B such that interference between the vehicles 7 at each interference point X is avoided.

[0119] Then, the virtual traffic rule generation unit 18 refers to the determination result regarding the abnormality of the approaching vehicle, and if an abnormality is detected in the approaching vehicle that may interfere with the compliant vehicle 7A at the interference point X in the global route of the compliant vehicle 7A, generates enhanced virtual traffic rules based on a fail-safe design concept that strengthen the constraints on movement so that the vehicle is less likely to interfere with the approaching vehicle at the interference point X than the virtual traffic rules generated before referring to the determination result regarding the abnormality of the approaching vehicle. The enhanced virtual traffic rules correspond to the strengthened constraint conditions according to this embodiment.

[0120] The virtual traffic rule generating unit 18 generates control information including a global route and virtual traffic rules for the compliant vehicle 7A, generates control information including virtual traffic rules for the semi-compliant vehicle 7B, and requests the communication unit 12 to transmit the control information associated with each of the compliant vehicle 7A and semi-compliant vehicle 7B. As a result, the control information associated with each of the compliant vehicle 7A and semi-compliant vehicle 7B is transmitted from the wireless communication device 3 to the corresponding compliant vehicle 7A and semi-compliant vehicle 7B.

[0121] The detailed method of generating the virtual traffic rules and the reinforced virtual traffic rules will be described later.

[0122] Next, an example of the configuration of the main parts of the electrical systems in the control device 10 and the control device of the vehicle 7 will be described.

[0123] 12 is a diagram showing an example of the configuration of a main part of an electrical system in the control device 10. As shown in FIG.

[0124] 6, 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 that is 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, the ROM 32, the RAM 33, the non-volatile memory 34, and the I / O 35 are connected to each other via a bus 36.

[0125] The nonvolatile memory 34 is an example of a storage device that maintains stored data even if the power supplied to the nonvolatile memory 34 is cut off, and is, for example, a semiconductor memory, but may also be a hard disk. The nonvolatile memory 34 does not necessarily need to be built into the computer 30, and may be a portable storage medium that is detachable from the computer 30, such as a USB (Universal Serial Bus) memory or a memory card.

[0126] To the I / O 35, for example, a communication unit 37, an input unit 38, and a display unit 39 are connected.

[0127] The communication unit 37 is connected to the communication network 5 and has a communication protocol for performing data communication between the wireless communication device 3 and the collection device 9, and between the communication network 5 and external devices connected thereto.

[0128] The input unit 38 is a unit that receives instructions from the operator of the control device 10 and notifies the CPU 31, and may be, for example, a button, a touch panel, a keyboard, a mouse, etc. When instructions are given by voice, a microphone may be used as the input unit 38.

[0129] 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, a projector, or the like.

[0130] In a situation where the control device 10 does not need to display information, such as when the control device 10 is installed in an unmanned data center, the display unit 39 may not be connected to the I / O 35. In addition, other units such as a printer unit may be connected to the I / O 35 as necessary.

[0131] 13 is a diagram showing an example of the configuration of a main part of an electrical system in a control device of a vehicle 7 other than a non-connected vehicle 7D. Like the control device 10, the control device of the vehicle 7 is also configured using a computer 40, for example.

[0132] 2 to 4, a ROM 42 that stores a control program that causes the computer 40 to function as a control device corresponding to each type of vehicle 7, a RAM 43 used as a temporary work area for the CPU 41, a non-volatile memory 44, and an I / O 45. The CPU 41, the ROM 42, the RAM 43, the non-volatile memory 44, and the I / O 45 are connected to each other via a bus 46.

[0133] To the I / O 45, 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.

[0134] The communication unit 47 has a communication protocol for performing data communication with the wireless communication device 3 .

[0135] The input unit 48 is a unit that receives instructions from the driver of the vehicle 7 and notifies the CPU 41, and may be, for example, a button, a touch panel, a pointing device, etc. When instructions are given by voice, a microphone may be used as the input unit 48.

[0136] The display unit 49 is a device that displays information processed by the CPU 41, and may be, for example, a liquid crystal display, an organic EL display, or a projector.

[0137] The traveling device 53 is a device that adjusts the amount of operation of operating devices that affect 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 a compliant vehicle 7A, the amount of operation is controlled by the CPU 41, and in the case of a semi-compliant vehicle 7B, the amount of operation is controlled by the driver. In the case of a non-compliant vehicle 7C, since it may or may not have an automatic driving function, the amount of operation of the operating device may be controlled by the CPU 41 or by the driver depending on the driving mode, but as already described, since an example in which the driver manually drives the non-compliant vehicle 7C is described, the amount of operation of the operating device of the non-compliant vehicle 7C in this embodiment is controlled by the driver.

[0138] 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 may be connected to the I / O 45. If the vehicle 7 is a semi-compliant vehicle 7B, at least one device that transmits control information to the driver is connected to the I / O 45. The functions of the internal sensor 51 and the external sensor 52 have already been explained, and in the case of a vehicle 7 that does not have an automatic driving function, such as the semi-compliant vehicle 7B, the external sensor 52 may not necessarily be connected to the I / O 45.

[0139] Next, the operation of the control device 10 in the control system 1 will be described in detail.

[0140] FIG. 14 is a flowchart showing an example of the flow of the control process executed by the CPU 31 of the control device 10 when moving body information is received from each vehicle 7.

[0141] A control program that defines the control processing is stored in advance, for example, in the ROM 32 of the control device 10. The CPU 31 of the control device 10 reads the control program stored in the ROM 32 and executes the control processing.

[0142] For ease of explanation, the operation of the control device 10 will be described below for the case where the vehicles 7 to be controlled by the control system 1 are vehicles P and vehicle Q. However, even if there are three or more vehicles 7 to be controlled, similar processing can be performed for each vehicle 7.

[0143] 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 body information. For convenience of explanation, the vehicle P is assumed to be a manual vehicle, and the vehicle Q is assumed to be an automatic vehicle.

[0144] In step S20, the CPU 31 generates a global route for the vehicle Q based on the location information of the vehicle Q included in the moving object information received from the vehicle Q, which is an automotive vehicle, and the destination of the vehicle Q.

[0145] In step S30, the CPU 31 generates a travel route for the vehicle P based on the position information of the vehicle P and the speed of the vehicle P included in the moving object information received from the vehicle P which is a manually-operated vehicle.

[0146] In addition, if the speed of the manual vehicle decelerates at a point just before the branch point, the probability of the vehicle turning is higher than going straight at the branch point, so the CPU 31 may generate only the route 6 that goes in the direction of the turn as the driving route for the vehicle P.

[0147] In step S40, the CPU 31 identifies a point where the waypoint indicating the route 6 of vehicle P comes close to within the interference range from the waypoint indicating the route 6 of vehicle Q, that is, an interference point X between vehicle P and vehicle Q. Specifically, the CPU 31 identifies, as the interference point X between vehicle P and vehicle Q, a waypoint at a point where the global route of vehicle Q acquired in step S20 (simply referred to as "route 6 of vehicle Q") and the driving route of vehicle P acquired in step S30 (simply referred to as "route 6 of vehicle P") intersect or join, or a waypoint in an area where the route 6 of vehicle P and the route 6 of vehicle Q run parallel to each other close enough to within the interference range.

[0148] In addition, when it is possible to refer to the interference point information as shown in Figure 9, if the routes 6 of each of vehicles P and vehicle Q include the same waypoint that is set as interference point X in the interference point information, the CPU 31 may set the point represented by the waypoint as the interference point X between vehicles P and Q.

[0149] In step S50, the CPU 31 calculates an interference time t between the vehicles P and Q for each interference point X between the vehicles P and Q identified in step S40.

[0150] The interference time t can be calculated from the distance along the route 6 from the current positions of the vehicles P and Q to the interference point X (hereinafter referred to as the "distance to the interference point X") and the speeds of the vehicles P and Q. However, because the vehicles P and Q cannot travel at a constant speed due to traffic conditions and the like, in reality the speeds of the vehicles P and Q fluctuate.

[0151] For example, if “v” is the speed of vehicle P and vehicle Q, “l” is the distance to interference point X, “α” and “β” are positive margins corresponding to the speed change of vehicle P and vehicle Q, and “ε” is a positive constant closer to 0 than other values, the interference time t at interference point X taking into account the fluctuation in the speed of vehicle P and vehicle Q is calculated by equation (1).

[0152]

number

[0153] In addition, t begin is the time when vehicle P and vehicle Q pass the interference point X earliest, and t end is the time when vehicle P and vehicle Q pass the interference point X latest. Thus, the interference time t of vehicle P and vehicle Q at each interference point X is t begin more than t end Hereinafter, the interference time t of vehicle P at interference point X is expressed as the "interference time t p " and the interference time t of vehicle Q at interference point X is expressed as "interference time tq The method of calculating the interference time t is not limited to the above, and prediction using a driver model or a vehicle behavior model may also be used.

[0154] FIG. 15 shows the interference time t p and the interference time t of vehicle Q q FIG.

[0155] In this manner, the CPU 31 calculates the interference time t p and the interference time t of vehicle Q q Calculate.

[0156] In step S60, the CPU 31 sets passage priorities for the vehicles P and Q at each of the interference points X identified in step S40.

[0157] A vehicle 7 having a high passing priority, i.e., a passing priority set as a priority, can pass through the interference point X with priority, whereas a vehicle 7 having a low passing priority, i.e., a passing priority set as a non-priority, is restricted from entering the interference point X during the interference time t at the interference point X of a vehicle 7 having a passing priority set as a priority.

[0158] In this way, by associating the passing priority with the interference time t at each interference point X and restricting the time and place in which vehicles P and Q can travel, interference between vehicles P and Q at each interference point X can be avoided.

[0159] The correspondence between the passing priority at each interference point X and the interference time t of the vehicle 7 whose passing priority is set to be priority, i.e., the no-entry time to the interference point X for the vehicle 7 whose passing priority is set to be non-priority, becomes the virtual traffic rule. The virtual traffic rule is an example of a spatiotemporal constraint that restricts the time and place where the vehicle 7 can travel by 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.

[0160] In the following, we will use the term “s” to indicate whether the passing priority at the interference point X is set to priority or not. x " The state s at the interference point X is x takes the value of "0" or "1", and the CPU 31 sets the state s x = 0, and if vehicle Q has priority, state s x Set =1.

[0161] Figure 16 shows the state s x 1 is a schematic diagram illustrating a hypothetical traffic rule at an interference point X where the traffic rule is set to 0.

[0162] In this case, the interference time t p t p begin more than t p end If the interference time is expressed as the time period until the interference time t p During the interference time t p If the vehicle enters the interference point X at the interference time t p During this time, it is necessary to avoid entering interference point X.

[0163] On the other hand, Figure 17 shows the state s x 1 is a schematic diagram illustrating a hypothetical traffic rule at an interference point X where the traffic rule is set to 1.

[0164] In this case, the interference time t q t q begin more than t q end If the interference time is expressed as the time period until the interference time t q During the interference time t q If vehicle Q enters interference point X at interference time t q During this time, it is necessary to avoid entering interference point X.

[0165] In other words, if a vehicle 7 whose passing priority at the interference point X is set to non-priority does not enter the interference point X during the interference time t of a vehicle 7 whose passing priority at the same interference point X is set to priority, the vehicles 7 can avoid interference with each other and reach the destination.

[0166] In this way, the virtual traffic rule is the state s x and the no-entry time at the interference point X (that is, the interference time t of the vehicle 7 having the priority of passing at the interference point X) are represented by information set for each interference point X.

[0167] At each interference point X, how to determine the passing priority of vehicle P and vehicle Q is performed by evaluating an objective function C(S).

[0168] The CPU 31 determines the passing priority of the vehicles P and Q using, for example, an objective function C(S) stored in advance in the non-volatile memory 34. The objective function C(S) is a function that represents the traffic cost incurred by the movement of the vehicles 7. The traffic cost is not only the cost for each vehicle 7, but also the cost that takes into account the impact that the travel of the vehicle 7 has on the travel of other vehicles 7, and represents the traffic efficiency of the vehicles 7.

[0169] Therefore, the CPU 31 sets the state vector S such 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.

[0170] In addition, “S” in the objective function C(S) is the state s at each interference point X when there are N interference points X between vehicles P and Q within the range managed by the control system 1 (N is a positive integer). x The set of s1, s2, s i , ,s N )

[0171] In the following, a method for determining the passing priority order of vehicles P and Q using objective function C(S) will be described.

[0172] The objective function C(S) is expressed, for example, by equation (2).

[0173]

number

[0174] Here, C single (S) is the cost that characterizes the control for vehicles P and Q passing through each interference point X. C consistency (S) is the cost related to the consistency in the time direction of the passing priority set for vehicles P and Q. C multi (S) is the cost of consistency and interaction between interference points X. ω c and ω m is the objective function C(S) consistency (S), and C multi This is a weight for adjusting the influence of (S).

[0175] In equation (2), 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).

[0176]

number

[0177] That is, C single (S) takes a low value when the passing priority of a vehicle 7 having an early interference time t at each interference point X, that is, a vehicle 7 having an early predicted passing time at the interference point X, is set as a high priority.

[0178] The CPU 31 determines the C taking into consideration the priority of the lane 8 on which the vehicle 7 is traveling, the length of the queue of vehicles on the lane 8 on which the vehicle 7 is traveling, the traffic density, the waiting time of the vehicle 7, and the priority of the vehicle 7 itself, such as an emergency vehicle. single For example, according to equation (4), if there is an interference point X i Even though the passing priority of vehicle P is set to be high at p is the interference time t q If slower, C single (s i ;t p ,t q ) is set to "1", and further, when the vehicle P is traveling on a non-priority road, C single (s i ;t p ,t q ) should be weighted to be greater than 1.

[0179] That is, when the priority passing order is set to a non-priority in a situation where it is considered that it is better to set the priority passing order to a priority, and when the priority passing order is set to a priority in a situation where it is considered that it is better to set the priority passing order to a non-priority, the CPU 31 single Weighting may be done so that (S) is larger.

[0180] In equation (2), C represents the cost of the time-wise consistency of the passing priority set for vehicle 7. consistency (S) is expressed by equations (5) and (6).

[0181]

number

[0182] Status x The optimal value is set every predetermined time unit (for example, 1 second) while referring to the mobile object information at that time along the time series. x (T) is the state s set at time T xrepresents s x (T-1) is the state s set at time T-1, one unit before time T. x Represents.

[0183] If the priority and non-priority indicating the passing priority of the same vehicle 7 are not frequently switched, the vehicle 7 can travel efficiently in the lane 8. Therefore, consistency (S) is the time series of adjacent states s x If the values ​​are the same, it is set to a smaller value, and if the values ​​are different, it is set to a larger value.

[0184] Here, as an example, the state s set at the time one unit before x (T-1) From the relationship with state s x (T) The CPU 31 calculates the cost of consistency when the state s at each time up to M units ago (M is a positive integer of 2 or more) is set. x From the relationship with state s x (T) Specifically, the CPU 31 calculates the number of times that priority and non-priority are switched for each interference point X during the period from time T to M units ago, and calculates the cost of consistency when C consistency To increase the value of (S), consistency (S) may be calculated.

[0185] In equation (2), C represents the cost of consistency and interaction between interference points X. multi (S) is expressed by equations (7) and (8).

[0186]

number

[0187] Here, w i,j There are two interference points X i , X j is a value that represents the degree of cooperation, and wi,j ≧0. The degree of cooperation of an interference point X represents the degree to which it is considered advisable to set the passage priority of one interference point X to the same state as the passage priority of the other interference point X in a coordinated manner.

[0188] w i,j =0 is interference point X i , X j indicates that there is no need for cooperation, and w i,j As the number of interference points X increases, i , X j This indicates that it is better to handle them in a coordinated manner.

[0189] Fig. 18 is a diagram showing an example in which it is better to handle multiple interference points X in a coordinated manner. As shown in Fig. 18, when a vehicle P traveling in a non-priority lane 8B crosses a priority lane 8A with one lane on each side in which another vehicle 7 is traveling and travels straight, the vehicle P needs to pass through interference points X1 and X2.

[0190] 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 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 disrupt the flow of vehicles 7 traveling in the priority lane 8A.

[0191] Therefore, when there are multiple interference points X at a location where it is better not to stop the vehicle 7 until it has passed, the state s of each interference point X is x It is better to set the same value to coordinate the interference points X with each other.

[0192] w for each combination of interference points X i,j is calculated in advance and stored in, for example, the non-volatile memory 34. The CPU 31 calculates w i,j Refer to C multi Just calculate (S).

[0193] Here, as an example, we use C based on the degree of cooperation between two interference points X. multi We have explained the calculation of (S), but we can use the cooperation degree of three or more interference points X to calculate C multi You may also perform the calculation of (S).

[0194] In this way, the objective function C(S) is configured by combining the contents of the traffic control for the vehicle 7, the consistency in the time direction of the passing priority at the interference point X, the cost reflecting the consistency and interaction between the interference points X, and the priority of the lane 8, but the objective function C(S) used by the CPU 31 to determine the passing priority at the interference point X for each vehicle 7 is not limited to the above-mentioned objective function C(S). An objective function C(S) reflecting other elements representing the traffic environment, safety, and traffic efficiency may be used.

[0195] 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), or may determine the state vector S that minimizes the objective function C(S) by using a known optimization method such as a gradient method. multi The effect of (S) on the objective function C(S) is C single (S) and C consistency If the effect of (S) on the objective function C(S) is smaller than the effect of (S) on the objective function C(S), the CPU 31 multi (S) into deleted subproblems to determine the state vector S that minimizes the objective function C(S). a ,S b ), but S a =(s1, ,s i ), S b =(s i+1 , ,s N ) and C multi (S)=C multi (S a )+C multi (S b ), the CPU 31 converts the objective function C(S) into a subproblem C(S a ) and C(S b ) and minimize each of them to find the state vector S a , Sb may be determined.

[0196] 14, the CPU 31 executes a virtual traffic rule generation process to generate virtual traffic rules for the vehicle Q, which is an automatic vehicle, and to generate virtual traffic rules for the vehicle P, which is a manual vehicle, if the vehicle P is a vehicle 7 that requires virtual traffic rules, that is, if the vehicle P is a semi-compliant vehicle 7B. The virtual traffic rule generation process will be described in detail later.

[0197] In step S80, the CPU 31 determines whether or not to generate control information depending on the types of the vehicles P and Q, and the contents of the control information.

[0198] Specifically, the CPU 31 generates control information including a global route and virtual traffic rules for the vehicle Q, which is an automated vehicle. When the vehicle P is a manual vehicle that requires virtual traffic rules, i.e., a semi-compliant vehicle 7B, the CPU 31 generates control information including virtual traffic rules for the vehicle P. When the vehicle P is a manual vehicle that does not require virtual traffic rules, i.e., a non-compliant vehicle 7C or a non-connected vehicle 7D, the CPU 31 does not generate control information for the vehicle P.

[0199] The CPU 31 performs control for transmitting the generated control information via the wireless communication device 3 to each of the vehicles 7 for which the control information was generated.

[0200] In this case, the CPU 31 grasps the position of the vehicle 7 for which the control information is generated from the latest moving body information, and designates the wireless communication device 3 that transmits the control information so that the control information is transmitted from the wireless communication device 3 that is closest to the current position of the vehicle 7. The position information of the wireless communication device 3 is stored in advance in, for example, the non-volatile memory 34.

[0201] This completes the control process shown in FIG.

[0202] The vehicles 7 that have received the traffic control information are driven in accordance with the virtual traffic rules contained in the traffic control information, so that interference between the vehicles 7 at the interference point X is avoided.

[0203] Next, a description will be given of the virtual traffic rule generation process in step S70 in Fig. 14, the outline of which has been mentioned above. Fig. 19 is a flowchart showing an example of the flow of the virtual traffic rule generation process.

[0204] In step S100, the CPU 31 calculates the collision points X between the vehicles P and Q identified in step S40 of FIG. 14, the collision times t at the collision points X calculated in step S50 of FIG. 14, and the states s at the collision points X set in step S60 of FIG. x Specifically, the CPU 31 generates a virtual traffic rule by using position information (e.g., an interference point ID) indicating the position of each interference point X and a state s x and state s x and generates a virtual traffic rule that associates the no-entry time for each interference point X obtained from the interference time t with the interference time t. The CPU 31 may include the range of the interference point X in the position information that indicates the position of each interference point X. The range of the interference point X is also called the "no-entry range 20" because it is a range in which the vehicle 7 cannot enter during the no-entry time.

[0205] If the vehicles P and Q enter the collision point X according to the generated virtual traffic rules, collision between the vehicles P and Q at the collision point X can be avoided. However, since the vehicle P is a manually operated vehicle, even if virtual traffic rules for the vehicle P are generated, the driver of the vehicle P will not necessarily drive the vehicle P according to the virtual traffic rules. Therefore, the speed of the vehicle P may suddenly change, and the collision time t p is the interference time t calculated in step S50 of FIG. p It is also expected that there will be deviations.

[0206] Fig. 20 is a diagram for explaining an example of a traffic flow caused by such a change in speed of vehicle P. In Fig. 20, vehicle P is traveling on a priority road, and vehicle Q is traveling on a non-priority road.

[0207] Vehicle Q, which travels according to virtual traffic rules in which the passing priority of vehicle P at interference point X is set to priority, stops just before interference point X and waits until vehicle P passes interference point X (see FIG. 20(A)).

[0208] In this situation, for example, if the vehicle P suddenly decelerates temporarily to avoid an obstacle, and the speed of the vehicle P at time T represented by the moving object information of the vehicle P suddenly drops below the speed of the vehicle P at time T-1, which is one unit before time T, the time t p begin As a result, a situation occurs in which vehicle Q continues to stop just before interference point X even though vehicle P does not pass through interference point X.

[0209] Therefore, it is assumed that the control device 10 generates new virtual traffic rules in which the passing priority of vehicle Q is set as a priority, and transmits the new virtual traffic rules to vehicle Q. Vehicle Q, which has received the new virtual traffic rules, starts traveling in accordance with the virtual traffic rules and enters the interference point X (see FIG. 20(B)).

[0210] On the other hand, if the vehicle P avoids the obstacle and is no longer hindered in its travel, and the vehicle P starts to pass the interference point X at a time t p begin Since the time of vehicle P is sooner, the control device 10 generates new virtual traffic rules in which the passing priority of vehicle P is set to a higher priority and transmits the new virtual traffic rules to vehicle Q.

[0211] As a result, vehicle Q may enter interference point X and stop while waiting for vehicle P to pass, thereby obstructing the passage of vehicle P (see FIG. 20(C)).

[0212] FIG. 21 is a diagram showing an example of virtual traffic rules for a vehicle Q that are generated in response to the speed change of the vehicle P described with reference to FIG.

[0213] Figure 21(A) is a graph showing the change in speed of vehicle P, where period IT1 represents the period before vehicle P temporarily decelerates suddenly, period IT2 represents the period during which vehicle P is decelerating suddenly, and period IT3 represents the period during which vehicle P accelerates and picks up speed.

[0214] Fig. 21(B) shows an example of the virtual traffic rules generated in step S100 of Fig. 19, in which the passing priority of vehicle Q is set to be higher than that of vehicle Q only during period IT2 in accordance with the speed change of vehicle P during period IT2. As explained above, if vehicle Q enters interference point X in accordance with the virtual traffic rules shown in Fig. 21(B), there is a possibility that vehicle Q will interfere with vehicle P due to the acceleration of vehicle P during period IT3.

[0215] Therefore, the control device 10 according to the present invention generates enhanced virtual traffic rules based on a fail-safe design concept that takes into account the worst-case scenario in which, if an unexpected state change occurs during period IT2, such as the vehicle P suddenly decelerating temporarily, thereafter the vehicle P may accelerate, by processing from step S110 onwards in FIG. 19.

[0216] Specifically, the control device 10 calculates the time when the vehicle P starts to pass the interference point X due to the deceleration of the vehicle P in the period IT2 as shown in FIG. 21(A) by calculating the time t p begin In this case, an enhanced virtual traffic rule as shown in FIG. 21C is generated so that the passing priority of the vehicle Q is not set to high priority in the period IT2 even if the vehicle Q is delayed further.

[0217] 19, the CPU 31 acquires the speed of the vehicle P from the moving object information of the vehicle P, which is an approaching vehicle relative to the vehicle Q and is a manual vehicle. The speed of the vehicle P is an example of a state value relating to the state of the approaching moving object approaching the vehicle Q. Since time is passing while the CPU 31 is acquiring the speed of the vehicle P, the moving object information of the vehicle P acquired in step S110 already represents the moving object information of the vehicle P at the time T-1, which is one unit earlier.

[0218] In step S120, the CPU 31 predicts the range of the speed that the vehicle P can assume at the time T one unit later, based on the moving object information of the vehicle P at the time T−1 one unit earlier acquired in step S110, and sets a prediction range of the speed of the vehicle P at the time T. The upper limit speed of the set prediction range of the speed is v upper and the lower limit speed is v lower In addition, the speed of the vehicle P at time T may be predicted using the speed and acceleration of the vehicle P at time T-1 and the amount of accelerator and brake operation by the driver, or the speed of the vehicle P at time T may be predicted by machine learning learning data that combines each past time and the speed of the vehicle P at that time using a support vector machine.

[0219] In step S130, the CPU 31 acquires the current speed of the vehicle P from the moving object information of the vehicle P acquired at the timing corresponding to the time T.

[0220] In step S140, the CPU 31 determines whether or not the current speed of the vehicle P acquired in step S130 is included in the prediction range of the speed set in step S120. If the current speed of the vehicle P is not included in the prediction range, the process proceeds to step S150.

[0221] 22 is a diagram showing an example of a state in which the current speed of the vehicle P is not included in the prediction range. The virtual traffic rule generated in step S100 of FIG. 19 is TThat is, as shown in FIG. 22, the current speed V of the vehicle P at time T is within the predicted range. T is the velocity V at time T-1 T-1 If the vehicle Q is traveling according to the virtual traffic rules generated in step S100 of FIG. 19, the current speed V of the vehicle P will be T is included in the prediction range, the probability that vehicle Q will interfere with vehicle P at interference point X or obstruct the passage of vehicle P at interference point X increases. Therefore, in step S150, the CPU 31 determines that an abnormality has occurred in vehicle P.

[0222] Since an abnormality has occurred in the vehicle P, in step S160, the CPU 31 generates reinforced virtual traffic rules.

[0223] For example, the CPU 31 generates an enhanced virtual traffic rule that strengthens the constraint by setting a no-entry time for vehicle Q longer than the virtual traffic rule for vehicle Q generated in step S100 in accordance with the change in speed of vehicle P, so that vehicle Q is less likely to interfere with vehicle P at the interference point X.

[0224] Fig. 23 is a diagram showing an example of the reinforced virtual traffic rule for a vehicle Q. As shown in Fig. 23, when the speed of a vehicle P is lower than the lower limit speed v lower If the vehicle Q is longer than the original time t, the CPU 31 generates an enhanced virtual traffic rule by extending the no-entry time for the vehicle Q by the extension time δ backward with respect to the virtual traffic rule for the vehicle Q generated in step S100. p end is the interference time t calculated before the abnormality of vehicle P is detected. p t p end represents the new t p end is the interference time t estimated from the speed of vehicle P when an abnormality of vehicle P is detected. p t p end It represents.

[0225] Also, if the speed of vehicle P is within the upper limit of the prediction range v upper If the vehicle P is faster than the predicted speed, the CPU 31 generates an enhanced virtual traffic rule for the vehicle Q generated in step S100 of Fig. 19 by extending the no-entry time for the vehicle Q forward by the extension time δ. Naturally, the CPU 31 may extend the no-entry time for the vehicle Q forward and backward by the extension time δ for the virtual traffic rule for the vehicle Q generated in step S100, taking into consideration the possibility that the speed of the vehicle P may continue to change irregularly beyond the predicted range. In this case, the CPU 31 may set the extension time δ for the forward extension and the extension time δ for the backward extension to different values.

[0226] With the above, the virtual traffic rule generation process shown in FIG. 19 is completed.

[0227] On the other hand, in the determination process of step S140 in FIG. 19, the current speed V T If it is determined that is within the prediction range, the process proceeds to step S170.

[0228] In this case, if vehicle Q travels according to the virtual traffic rules for vehicle Q generated in step S100, it is possible to avoid interference with vehicle P at interference point X. Therefore, in step S170 in Fig. 19, CPU 31 determines that no abnormality has occurred in vehicle P and that vehicle P is normal. In addition, since no abnormality has occurred in vehicle P, there is no need to generate enhanced virtual traffic rules, and therefore CPU 31 ends the virtual traffic rule generation process shown in Fig. 19 without generating enhanced virtual traffic rules in step S160.

[0229] In the virtual traffic rule generation process shown in FIG. 19, the speed of the vehicle P is used as a state value related to the state of the vehicle P to be judged for judging whether or not an abnormality has occurred in the vehicle P approaching the vehicle Q. However, instead of the speed of the vehicle P, for example, the acceleration of the vehicle P, the passing time t p, and the position of the vehicle P. In addition, the CPU 31 may use, for example, the speed of the vehicle P, the acceleration of the vehicle P, the passing time t p A prediction range may be set for each of multiple state values, such as the position of vehicle P, and if any one of the state values ​​is not included in the prediction range, or if a predetermined number of state values ​​(e.g., two) are not included in the prediction range, it may be determined that an abnormality has occurred in vehicle P.

[0230] In predicting each state value that is the subject of judgment regarding abnormalities in vehicle P, not only the moving body information of vehicle P but also the traffic regulation information contained in the controlled driving route map 19 may be used, such as information that represents the regulations and shape of the road on which vehicle P is traveling, such as the speed limit, number of lanes, curvature, width, gradient, and lane priority of the road on which vehicle P is traveling.

[0231] In addition, when the vehicle P is a non-connected vehicle 7D, the speed of the vehicle P, the acceleration of the vehicle P, the passing time t p The state values ​​that are used to determine whether or not there is an abnormality in the vehicle P, such as the vehicle speed, the vehicle position, and the like, are measured based on images captured by the collection device 9. Therefore, as the image quality of the images captured by the collection device 9 deteriorates, the measurement accuracy of the state values ​​that are used to determine whether or not there is an abnormality in the vehicle P also deteriorates. If the presence or absence of an abnormality in the vehicle P is determined using state values ​​with reduced measurement accuracy, the reliability of the determination result also deteriorates.

[0232] Therefore, even if the CPU 31 determines in step S140 of FIG. 19 that the state value to be judged as an abnormality of the vehicle P is within the predicted range, if it is considered that the measurement accuracy of the state value is lower than a predetermined standard accuracy, it is preferable for the CPU 31 to determine that an abnormality has occurred in the vehicle P based on the fail-safe design concept.

[0233] Image quality is affected by weather conditions: for example, images captured by the collection device 9 during rain or fog may have lower quality than images captured by the collection device 9 during clear skies.

[0234] Furthermore, the image quality is affected by the operating state of the collecting device 9. For example, the image quality of an image captured when some kind of failure occurs in the collecting device 9 may be lower than the image quality of an image captured when no failure occurs.

[0235] Therefore, if at least one of the following events occurs: the amount of rainfall in the area where the collecting device 9 is installed is greater than a reference value, the occurrence of fog is notified in the area where the collecting device 9 is installed, and a malfunction notification is notified from the collecting device 9, the CPU 31 may determine that the measurement accuracy of the state value that is to be used to determine whether or not an abnormality exists in the vehicle P is lower than a predetermined reference accuracy. The CPU 31 acquires meteorological information from, for example, an external device connected to the communication network 5.

[0236] In addition, when the error between the predicted state value and the actual state value is larger than a predetermined value, the CPU 31 may determine that the measurement accuracy of the state value is lower than a predetermined reference accuracy.

[0237] In addition, even if vehicle P is a semi-compliant vehicle 7B or a non-compliant vehicle 7C whose status values, which are used to determine whether vehicle P is abnormal, are not measured based on images captured by the collection device 9, if the error between the predicted status value and the actual status value is greater than a predetermined value, the CPU 31 may determine that the measurement accuracy of the status value is lower than a predetermined standard accuracy.

[0238] Up to this point, an example has been described in which, when generating the enhanced virtual traffic rule in step S160 in Fig. 19, the no-entry time in the virtual traffic rule for vehicle Q generated in step S100 in Fig. 19 is extended so as to make it less likely for vehicles P and Q to interfere with each other at the interference point X. However, instead of extending the no-entry time, the CPU 31 may set the range of the interference point X, i.e., the no-entry range 20 for vehicle Q, to be wider than the no-entry range 20 represented by the virtual traffic rule for vehicle Q generated in step S100.

[0239] Fig. 25 is a diagram showing an example of a no-entry area 20 set centered around the interference point X. In Fig. 25, the no-entry area 20A represents the no-entry area 20 represented by the virtual traffic rules for vehicle Q generated in step S100 of Fig. 19, and the no-entry area 20B represents the no-entry area 20 represented by the enhanced virtual traffic rules. In this way, by setting the no-entry area 20B wider than the no-entry area 20A, it becomes less likely that vehicles P and Q will interfere with each other at the interference point X.

[0240] Furthermore, the CPU 31 may adjust the strength of the constraint in the reinforced virtual traffic rule according to the number of times an abnormality in the vehicle P is detected.

[0241] The fact that an abnormality has been detected multiple times for the same vehicle P means that the driver of the vehicle P has a tendency to frequently change lanes 8 or to repeatedly make sudden stops and starts. In other words, the more abnormalities detected for a vehicle P, the more difficult it is to predict the driving state of the vehicle P. Therefore, even if vehicle Q drives in accordance with the virtual traffic rules, it is more likely to interfere at the interference point X than a vehicle P that has never detected an abnormality.

[0242] Therefore, it is preferable that the CPU 31 generates enhanced virtual traffic rules with stronger constraints so that the vehicle Q is less likely to interfere with the vehicle P at the interference point X than the virtual traffic rules for the vehicle Q generated in step S100 of Fig. 19 as the number of times an abnormality of the vehicle P is detected increases. For example, the CPU 31 may set the extension time δ to be longer or the no-entry area 20 to be wider as the number of times an abnormality of the vehicle P is detected increases.

[0243] <Modification of enhanced virtual traffic rule generation> When vehicle P, which is a manual vehicle, is a connected vehicle that drives in accordance with the received virtual traffic rules, i.e., when vehicle P is a compliant vehicle 7A or a semi-compliant vehicle 7B, CPU 31 may generate enhanced virtual traffic rules not only for vehicle Q but also for vehicle P.

[0244] By generating the reinforced virtual traffic rule for the vehicle P and the vehicle Q, the vehicles P and Q are less likely to interfere with each other at the interference point X than when the reinforced virtual traffic rule is generated only for the vehicle Q.

[0245] FIG. 26 is a diagram showing an example of an enhanced virtual traffic rule that is generated when an abnormality is detected in vehicle P in a situation in which the passing priority of vehicle P is set to priority, as shown in FIG.

[0246] As shown in FIG. 26, the CPU 31 determines an interference time t p and interference time t q A reinforced virtual traffic rule is generated with the no-entry time set to include the overlap period where the vehicle P and the vehicle B overlap. The no-entry time for vehicle P is represented as “τ”, and τ=t p end -t q begin +τ0, where “τ0” is a safety margin and takes a real value equal to or greater than 0. As the safety margin τ0 increases, the time during which vehicle P is prohibited from entering the interference point X becomes longer, so that the probability of interference between vehicle P and vehicle Q at interference point X can be further reduced.

[0247] The safety margin τ0 is a value that is set taking into consideration information that indicates the shape of the road on which the vehicle P is traveling and the visibility at the interference point X. For example, in the case of the interference point X with poor visibility, the safety margin τ0 is set to be larger than a reference value. Furthermore, the safety margin τ0 does not need to be a fixed value, and for example, in the case where it is considered that it is raining in an area around the road on which the vehicle P is traveling and the visibility at the interference point X is poor, the safety margin τ0 may be further increased. Furthermore, the CPU 31 may adjust the size of the safety margin τ0 depending on the time of day when the vehicle P is traveling, for example, by making the safety margin τ0 at night larger than the safety margin τ0 during the day.

[0248] On the other hand, the CPU 31 generates an enhanced virtual traffic rule for the vehicle Q by extending the no-entry time for the vehicle Q by an extension time δ with respect to the virtual traffic rule for the vehicle Q generated in step S100 of Fig. 19. In this case, the extension time δ is expressed as δ=max(τ, δ min That is, the extension time δ is expressed as a sum of the no-entry time τ for the vehicle P and the lower limit value δ of the extension time δ that is previously set. min The time is set to the longer of the two.

[0249] The lower limit value δ of the extension time δ min Similarly to the safety margin τ0, the lower limit δ is set by taking into account the shape of the road on which the vehicle Q is traveling and the visibility conditions. For example, in the case of an interference point X with poor visibility, the lower limit δ min It is preferable to set the lower limit δ to be larger than the reference value. min Similarly to the safety margin τ0, the lower limit δ does not need to be a fixed value, and can be adjusted according to the weather conditions around the interference point X and the time period when the vehicle Q is traveling. min may be adjusted.

[0250] As already explained, in the virtual traffic rule generated in step S100 of FIG. 19, when the passing priority of the vehicle P at the interference point X is set to priority, the vehicle P passes through the interference time t p The collision point X is set as accessible throughout the entire area, and vehicle Q is allowed to enter the collision point X for a collision time t p An exclusive setting is made such that entry to interference point X is prohibited throughout the entire area.

[0251] In contrast, when the present modified example relating to the generation of the reinforced virtual traffic rule is applied, the interference time t p A time period during which both vehicle P and vehicle Q are prohibited from entering the interference point X is set within the time period during which vehicle P and vehicle Q are prohibited from entering the interference point X. In this manner, by generating an enhanced virtual traffic rule that is set so that the no-entry times for vehicle P and vehicle Q overlap, even if either vehicle 7 of vehicle P and vehicle Q erroneously enters the interference point X without following the enhanced virtual traffic rule, it is possible to avoid interference between the vehicles 7 at the interference point X.

[0252] The interference time t p The further the vehicle P is from the interference point X, the longer the interference time t p The closer to the interference point X, the longer the interference time t p The calculation accuracy of is improved and the interference time t q The same can be said about the interference time t p and interference time t q The overlap period during which the vehicles P and Q overlap becomes shorter as the vehicles P and Q approach the interference point X. p and interference time t q If the overlap period between the intersections becomes shorter, the no-entry time τ for vehicle P also becomes shorter accordingly, and vehicle P traveling according to the reinforced virtual traffic rules will behave as if it stops just before the interference point X and then starts moving immediately.

[0253] Since such a short stop of about several seconds interferes with the smooth running of the vehicle P, the CPU 31 sets the interference time t p and interference time t q The generated content of the reinforced virtual traffic rule may be changed depending on the length of the overlapping period.

[0254] Specifically, in a situation where the passing priority of the vehicle P at the interference point X is set to be high, the no-entry time τ for the vehicle P is equal to or exceeds a predetermined threshold τ th If the time t1 exceeds the extension time δ, as shown in FIG. 26, the CPU 31 generates an enhanced virtual traffic rule that sets a no-entry time τ for the vehicle P, and generates an enhanced virtual traffic rule that extends the no-entry time for the vehicle Q by an extension time δ for the vehicle Q.

[0255] On the other hand, in a situation where the passing priority of vehicle P at interference point X is set to be high, the entry prohibition time τ for vehicle P is set to be higher than the threshold τ thIn the following case, the CPU 31 does not generate a reinforced virtual traffic rule for the vehicle P, but generates a reinforced virtual traffic rule for only the vehicle Q, which extends the no-entry time of the vehicle Q by the extension time δ. That is, the CPU 31 does not set the no-entry time τ for the vehicle P, but instead extends the no-entry time of the vehicle Q by the extension time δ.

[0256] Note that the no-entry time τ is a threshold τ th When vehicle P approaches vehicle Q to a position such that:

[0257] FIG. 27 shows the relationship between the no-entry time τ and the threshold τ th 19 is a diagram showing an example of constraint conditions that restrict the traveling of each of vehicles P and Q in the following cases. As the constraint conditions of vehicle P, the virtual traffic rules of vehicle P generated in step S100 of Fig. 19 are applied, and as the constraint conditions of vehicle Q, enhanced virtual traffic rules in which the no-entry time for vehicle Q is extended by an extension time δ with respect to the virtual traffic rules of vehicle Q generated in step S100 of Fig. 19 are applied.

[0258] In the present modified example of generating the reinforced virtual traffic rule, the CPU 31 also sets the lower limit value δ of the extension time δ so that the extension time δ becomes longer as the number of times an abnormality of the vehicle P is detected increases. min and the safety margin τ0 may be adjusted.

[0259] So far, the modified example relating to the generation of the reinforced virtual traffic rule in a situation where the passing priority of the vehicle P at the interference point X is set to priority has been described. However, even in a case where the passing priority of the vehicle P at the interference point X is set to non-priority, the CPU 31 may set the interference time t p and interference time t q An enhanced virtual traffic rule is generated in which the no-entry time is set so as to include the overlapping period where the two traffic rules overlap.

[0260] FIG. 28 is a diagram showing an example of the reinforced virtual traffic rule that is generated when an abnormality is detected in the vehicle P in a situation where the passing priority order of the vehicle P is set to non-priority.

[0261] In this case, the CPU 31 determines the interference time t p and interference time t q 19. On the other hand, for vehicle P, the CPU 31 generates an enhanced virtual traffic rule that extends the no-entry time of vehicle P by an extension time δ with respect to the virtual traffic rule for vehicle P generated in step S100 of FIG.

[0262] Naturally, the no-entry time τ for vehicle Q is equal to or smaller than the threshold τ th If the condition is equal to or less than the above, the CPU 31 does not generate an enhanced virtual traffic rule for the vehicle Q as shown in FIG. 29, but generates an enhanced virtual traffic rule for only the vehicle P by extending the no-entry time for the vehicle P by the extension time δ.

[0263] In the present embodiment, an example has been described in which the control system 1 controls the vehicle 7, but the object of control by the control system 1 is not limited to the vehicle 7, and may be any moving object. Moving objects include pedestrians, bicycles, motorcycles, wheelchairs, kickboards, ships, and flying objects such as drones.

[0264] Like vehicle 7, these moving bodies are classified into compliant moving bodies, semi-compliant moving bodies, non-compliant moving bodies, and non-connected moving bodies depending on the functions possessed by the moving bodies and the state of the moving bodies.

[0265] 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 modifications or improvements can be made to the embodiments without departing from the gist of the present invention, and the forms in which such modifications or improvements are made are also included in 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.

[0266] In the embodiment, the control process in the control device 10 is realized by software as an example, but the process equivalent to the flowcharts shown in Fig. 14 and Fig. 19 may be implemented in, for example, 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, the process can be performed at a higher speed than when each process is realized by software.

[0267] In this way, the CPU 31 of the control device 10 and the CPU 41 in the control device of the vehicle 7 may be replaced with a dedicated processor specialized for specific processing, such as an ASIC, FPGA, PLD, GPU (Graphics Processing Unit), and FPU (Floating Point Unit).

[0268] In addition, the operations of the CPU 31 in the control device 10 and the CPU 41 in the control device of the vehicle 7 in the embodiment may be realized by a single CPU 31 and a single CPU 41, respectively, or may be realized by a plurality of CPUs 31 and a plurality of CPUs 41. Furthermore, the operations of the CPU 31 in the control device 10 and the CPU 41 in the control device of the vehicle 7 in the embodiment may be realized by cooperation of the CPU 31 in the computer 30 and the CPU 41 in the computer 40, which are located at physically separate locations.

[0269] 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. However, the present invention is not limited to this. The control program and the control program according to the present invention can be provided in a form recorded in a storage medium readable by the computer 30 and the computer 40. For example, the control program and the control program may be provided in a form recorded in an optical disk such as a CD (Compact Disc)-ROM or a DVD (Digital Versatile Disc)-ROM. The control program and the control program may be provided in a form recorded in a portable semiconductor memory such as a USB (Universal Serial Bus) memory or a memory card. The ROM 32, the ROM 42, the non-volatile memory 34, the non-volatile memory 44, the CD-ROM, the DVD-ROM, the USB, and the memory card are examples of non-transitory storage media.

[0270] 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. In this case, the CPU 31 of the control device 10 and the CPU 41 in the control device of the vehicle 7 read the control program and the control program downloaded from the external device, respectively, and execute the control process and the control process of the vehicle 7.

[0271] The following supplementary notes are further disclosed regarding the above embodiment.

[0272] (Additional note 1) Memory, at least one processor coupled to the memory; Including, The processor, receiving mobile unit information representing a state of each mobile unit including an autonomous mobile unit that receives constraint conditions defining a travel time and space; Detecting whether or not an abnormality has occurred in the movement of an approaching mobile object from a change in state of the approaching mobile object that may approach within a predetermined range of the autonomous mobile object in the interference area among the mobile objects that have received the mobile object information; When an abnormality is detected, the constraint condition is generated based on the abnormality detection unit before the abnormality is detected. The entry prohibition time for prohibiting entry into the interference area is lengthened, A reinforced constraint condition is generated for the autonomous moving body, which is a constraint condition that strengthens the constraint so that the approaching moving body is less likely to interfere with the autonomous moving body in the interference region. Control device.

[0273] (Additional note 2) A non-transitory storage medium storing a program executable by a computer to execute a virtual traffic rule generation process, The virtual traffic rule generation process includes: among each of the moving bodies that have received moving body information representing a state of the moving body, including an autonomous moving body that receives constraint conditions that define a moving time and space, detecting whether or not an abnormality has occurred in the movement of an approaching moving body from a change in state of the approaching moving body that may approach the autonomous moving body within a predetermined range in an interference area; When an abnormality is detected, the constraint condition is generated based on the abnormality detection unit before the abnormality is detected. The entry prohibition time for prohibiting entry into the interference area is lengthened, A reinforced constraint condition is generated for the autonomous moving body, which is a constraint condition that strengthens the constraint so that the approaching moving body is less likely to interfere with the autonomous moving body in the interference region. Non-transitory storage medium. [Explanation of symbols]

[0274] 1 Control system, 3 Wireless communication device, 5 Communication network, 6 (6P, 6Q) Route, 7 Vehicle, 7A Compliant vehicle, 7B Semi-compliant vehicle, 7C Non-compliant vehicle, 7D Non-connected vehicle, 8 Lanes, 8A Priority lane, 8B Non-priority lane, 9 Collection device, 9A Infrastructure sensor, 9B Status detection unit, 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 Anomaly detection unit, 18 Virtual traffic rule generation unit, 19 Controlled travel route map, 20 (20A, 20B) No entry area, 21A (21B, 21C) Position estimation unit, 22A (22B, 22C) Status management unit, 23A (23B, 23C) Wireless communication unit, 24 Local route planning unit, 25A (25B, 25C) control unit, 26 vehicle driving route map, 27 virtual traffic rule transmission unit, 30 (40) computer, 31 (41) CPU, 32 (42) ROM, 33 (43) RAM, 34 (44) non-volatile memory, 35 (45) I / O, 36 (46) bus, 37 (47) communication unit, 38 (48) input unit, 39 (49) display unit, 51 internal sensor, 52 external sensor, 53 driving device, δ extension time, δ min Lower limit of extension time, τ No entry time, τ0 Safety margin, τ th No-entry time threshold, C(S) objective function, K1~K7 points, S state vector, X interference point

Claims

1. A receiving unit that receives moving body information representing a state of each moving body including an autonomous moving body that receives constraint conditions that define a moving time and space; an anomaly detection unit that detects whether or not an anomaly has occurred in the movement of an approaching mobile body from a change in state of the approaching mobile body that may approach within a predetermined range of the autonomous mobile body in an interference area, among the mobile bodies whose information has been received by the receiving unit; a generation unit that generates, when an abnormality is detected by the abnormality detection unit, for the autonomous moving body, a reinforced constraint condition that is a constraint condition that strengthens the constraint so that the autonomous moving body is less likely to interfere with the approaching moving body in the interference area by making an entry prohibition time that prohibits entry into the interference area longer than the constraint condition generated before the abnormality is detected by the abnormality detection unit; and A control device equipped with

2. The abnormality detection unit detects that an abnormality has occurred in the movement of the approaching mobile object when a state value relating to the state of the approaching mobile object is not included in a prediction range set using mobile object information of the approaching mobile object received by the receiving unit. The control device according to claim 1.

3. The abnormality detection unit detects that an abnormality has occurred in the movement of the approaching mobile object when the state value is not included within the prediction range set for at least one of the states of the approaching mobile object, namely, the speed of the approaching mobile object, the acceleration of the approaching mobile object, the passing time of the approaching mobile object passing through the interference area, and the position of the approaching mobile object. The control device according to claim 2.

4. The abnormality detection unit detects that an abnormality has occurred in the movement of the approaching mobile body when the measurement accuracy of the state value relating to at least one of the state of the approaching mobile body, i.e., the speed of the approaching mobile body, the acceleration of the approaching mobile body, the passing time of the approaching mobile body passing through the interference area, and the position of the approaching mobile body, is lower than a predetermined reference accuracy, even if the state value is included in the prediction range. The control device according to claim 2.

5. The generation unit adjusts, by using a shape of the interference region, an extension time of the no-entry time into the interference region in the reinforced constraint condition, which is extended with respect to the no-entry time into the interference region in the constraint condition generated before the abnormality is detected by the abnormality detection unit. A control device according to any one of claims 1 to 4.

6. When the approaching moving body is a connected moving body that receives the constraint condition generated by the generation unit and moves in accordance with the received constraint condition, The generation unit generates the reinforced constraint condition for each of the autonomous moving body and the connected moving body, the reinforced constraint condition being set so that a no-entry time period into the interference region for each of the autonomous moving body and the connected moving body overlaps. A control device according to any one of claims 1 to 5.

7. The generation unit generates, as the number of times an abnormality related to the movement of the approaching moving body increases, the reinforced constraint condition for the autonomous moving body in which a time period during which entry into the interference area is prohibited is set longer than the constraint condition generated before the abnormality is detected by the abnormality detection unit. A control device according to any one of claims 1 to 6.

8. The generation unit generates the reinforced constraint condition for the autonomous moving body so that a range in which entry of the autonomous moving body is prohibited is wider than the constraint condition generated before the abnormality is detected by the abnormality detection unit. A control device according to any one of claims 1 to 7.

9. The generation unit generates, as the number of times an abnormality related to the movement of the approaching moving body increases, a reinforced constraint condition for the autonomous moving body in which a range of the interference area in which entry of the autonomous moving body is prohibited is set wider than the constraint condition generated before the abnormality is detected by the abnormality detection unit. The control device according to claim 8.

10. an acquisition device that acquires, in an interference area, moving body information representing a state of an approaching moving body that may approach within a predetermined range an autonomous moving body that receives a constraint condition that specifies a moving time and a space, and the moving body information representing a state of the autonomous moving body; a receiving unit that receives, from the acquisition device, moving body information of the autonomous moving body and the approaching moving body acquired by the acquisition device; an abnormality detection unit that detects whether or not an abnormality has occurred in the movement of the approaching moving body based on a change in the state of the approaching moving body represented by the moving body information received by the receiving unit; and a generating unit that, when an abnormality is detected by the abnormality detection unit, generates for the autonomous moving body a strengthened constraint condition, which is the constraint condition that strengthens the constraint so that the approaching moving body is less likely to interfere with the interference area by making an entry prohibition time that prohibits entry into the interference area longer than the constraint condition generated before the abnormality was detected by the abnormality detection unit. A control system equipped with

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