Signal control devices, signaling devices, systems and programs

Abstract

To suppress occurrence of delay in a travel plan of an automatic driving vehicle.SOLUTION: A first acquisition unit 24 of a signal control device 22 acquires a traffic situation around an intersection for control from a sensor 110 provided around the intersection for control, a second acquisition unit 26 acquires a travel plan of an automatic driving vehicle 16 which is scheduled to pass through the intersection for control, a determination unit 28 determines whether or not a delay in the travel plan of the automatic driving vehicle 16 occurs when the automatic driving vehicle 16 passes through the intersection for control on the basis of the traffic situation acquired by the first acquisition unit 24, and a control unit 30 controls a traffic light 100 of the intersection for control so that the delay is suppressed when it is determined that the delay occurs by the determination unit 28.SELECTED DRAWING: Figure 15

Description

[Technical Field]

[0001] The present invention relates to a signal control device, a traffic signal device, a traffic signal system, and a signal control program. [Background Art]

[0002] Patent Document 1 describes an autonomous driving vehicle equipped with an autonomous driving function. [Prior Art Literature] [Patent Literature]

[0003] [Patent Document 1] Japanese Unexamined Patent Application Publication No. 2022-035198 [Summary of the Invention] [Problem to be Solved by the Invention]

[0004] Generally, when a destination is set, an autonomous driving vehicle subdivides the route to the destination into scheduled travels such as going straight through an intersection or turning left / right, creates a travel plan that also determines the scheduled execution time for each scheduled travel, and travels on a road through autonomous driving in accordance with the created travel plan. However, depending on the traffic conditions of the road on which the autonomous driving vehicle travels, there is a possibility that a delay may occur in the travel plan of the autonomous driving vehicle. Examples of traffic conditions that may cause a delay in the travel plan include a case where the vehicle encounters an emergency vehicle or the like, and a case where a pedestrian is present when the vehicle turns left or right at an intersection. If a delay occurs in the travel plan of the autonomous driving vehicle, a large load such as re-creating the travel plan is applied to an on-board computer that performs autonomous driving control or the like during traveling, which is not preferable.

[0005] The present disclosure has been made in consideration of the above facts, and an object of the present disclosure is to obtain a signal control device, a traffic signal device, a traffic signal system, and a signal control program that can suppress the occurrence of a delay in the travel plan of an autonomous driving vehicle. [Means for Solving the Problem]

[0006] The signal control device according to the first embodiment is provided by sensors installed around the intersection. 、 Traffic conditions around the aforementioned intersection This includes information including at least one of the following: whether or not an emergency vehicle is about to pass through the intersection, and whether or not there are pedestrians who may interfere with the autonomous vehicle when it turns right or left at the intersection. The first acquisition unit to be acquired, and the planned passage through the aforementioned intersection. The aforementioned A second acquisition unit acquires the driving plan of the autonomous vehicle, and based on the traffic conditions acquired by the first acquisition unit, the autonomous vehicle by The aforementioned intersection of passing The scheduled time Driving plan of the aforementioned autonomous vehicle against The system includes a determination unit that determines whether or not a delay will occur, and a control unit that controls the traffic signals at the intersection to suppress the delay if the determination unit determines that a delay will occur.

[0007] In the first embodiment, sensors installed around the intersection 、 Traffic conditions around the intersection The system acquires information including at least one of the following: whether an emergency vehicle is attempting to pass through the intersection, and whether there are pedestrians who may interfere with the autonomous vehicle when it turns right or left at the intersection, and the acquired traffic conditions Based on this, the autonomous vehicle is scheduled to pass through the aforementioned intersection. by The aforementioned intersection of passing The scheduled time Driving plan for autonomous vehicles against Determine whether or not a delay will occur. Then, in the first embodiment, the driving plan of the autonomous vehicle. against If a delay is detected, the traffic lights at the intersection are controlled to suppress delays in the autonomous vehicle's driving plan. This prevents delays in the autonomous vehicle's driving plan and reduces the heavy load on the onboard computer that performs autonomous driving control, such as the need to recreate the driving plan, during operation.

[0008] In a second embodiment, in the first embodiment, the control unit controls the traffic lights at the intersection so that they remain green while the autonomous vehicle is passing through the intersection, when the determination unit determines that the delay has occurred.

[0009] In the second embodiment, traffic signals at an intersection are controlled to suppress delays in the autonomous vehicle's driving plan by keeping the traffic signals green while the autonomous vehicle is passing through the intersection. This ensures the safety of the autonomous vehicle as it passes through the intersection while preventing the traffic signals from remaining green for an unnecessarily long period, compared to controlling the traffic signals by extending the time they remain green for a certain period of time.

[0010] A third aspect is that, in the first aspect, an automated driving vehicle in which the control unit controls the traffic signals at the intersection in such a way that the delay is suppressed is an automated driving vehicle in which the preset urgency level is equal to or greater than a predetermined value.

[0011] According to the third embodiment, delays in the driving plan can be suppressed for autonomous vehicles with an urgency level of a predetermined value or higher, and the number of times traffic lights at intersections are controlled can be reduced, thereby also reducing the number of other vehicles besides autonomous vehicles with an urgency level of a predetermined value or higher whose driving may be affected by the control of traffic lights at intersections.

[0012] A fourth aspect is when, in the first aspect, the determination unit determines that the delay occurs. The system then determines whether the delay has been resolved by controlling the traffic signals at the intersection, and if it determines that the delay has not been resolved, it controls the traffic signals at the next intersection so that the signal at that intersection remains green while the automated vehicle is passing through the next intersection it is scheduled to pass through. It further includes a cooperative control unit.

[0013] In the fourth aspect, when it is determined that a delay occurs in the driving plan of the autonomous vehicle, In addition, the system determines whether the delay has been resolved by controlling the traffic signals at the intersection. If it determines that the delay has not been resolved, the system controls the traffic signals at the next intersection so that the signal remains green while the autonomous vehicle is passing through that next intersection. Therefore, it becomes possible to eliminate delays in the autonomous vehicle's driving plan while the autonomous vehicle sequentially passes through the multiple intersections.

[0014] The traffic signal system according to the fifth embodiment includes a traffic signal control device according to any of the first to third embodiments and the traffic signal, and is installed at each intersection.

[0015] In the fifth embodiment, since it includes a signal control device of any of the first to third embodiments, it is possible to suppress delays in the driving plan of the autonomous vehicle, similar to the first embodiment.

[0016] A traffic light system according to a sixth aspect includes the traffic light devices according to claim 1 each provided at a plurality of intersections, and when any one of the plurality of traffic light devices has the determination unit that determines that said delay occurs The system determines whether the delay has been resolved by controlling the traffic signals at any of the intersections where the determination unit is installed. If it determines that the delay has not been resolved, it controls the traffic signals at the next intersection so that the signal remains green while the automated vehicle is passing through that next intersection. a cooperative control unit.

[0017] In the sixth aspect, since a cooperative control device having the same configuration as the cooperative control unit of the fourth aspect is included, similarly to the fourth aspect, it becomes possible to eliminate the delay in the travel plan of an autonomous driving vehicle while the autonomous driving vehicle sequentially passes through the plurality of intersections.

[0018] A signal control program according to a seventh aspect causes a computer to, from a sensor provided around an intersection 、 traffic conditions around said intersection This includes information including at least one of the following: whether or not an emergency vehicle is about to pass through the intersection, and whether or not there are pedestrians who may interfere with the autonomous vehicle when it turns right or left at the intersection. acquire the information, and acquire a travel plan of the autonomous driving vehicle scheduled to pass through said intersection The aforementioned acquire the travel plan of the autonomous driving vehicle, and based on the acquired traffic conditions, the autonomous driving vehicle by said intersection of passing The scheduled time the travel plan of said autonomous driving vehicle against determine whether a delay will occur, and when it is determined that a delay will occur, execute processing including controlling the traffic light at said intersection such that the delay is suppressed.

[0019] According to the seventh aspect, similarly to the first aspect, occurrence of a delay in the travel plan of an autonomous driving vehicle can be suppressed.

[0020] It should be noted that the above summary of the present disclosure does not list all necessary features of the present disclosure. Subcombinations of these feature groups may also constitute the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] [Figure 1] FIG. 1 is a schematic diagram showing the risk prediction capability of AI for ultra-high-performance autonomous driving. [Figure 2]This is a block diagram illustrating the Central Brain in ultra-high-performance autonomous driving. [Figure 3] This is a plan view illustrating the blind spots of a vehicle. [Figure 4] This is a schematic diagram illustrating the sensors installed in traffic signals. [Figure 5] This is a schematic diagram illustrating Perfect Speed ​​Control. [Figure 6] This is a schematic diagram illustrating Perfect Bell Curves. [Figure 7] This is an overview diagram of Perfect Cruising. [Figure 8] This is an overview diagram of Perfect Cruising. [Figure 9] This is an overview diagram of Perfect Cruising. [Figure 10] This is an overview diagram of Perfect Cruising. [Figure 11] This is an overview diagram of Perfect Cruising. [Figure 12] This is an overview diagram of Perfect Cruising. [Figure 13] This is an overview diagram of Perfect Cruising. [Figure 14] This block diagram schematically shows an example of the hardware configuration of a computer that functions as a central brain or control unit. [Figure 15] This is a block diagram showing the schematic configuration of the signal control system according to the second embodiment. [Figure 16] This is a flowchart showing an example of signal control processing. [Figure 17] This is a timing chart used to explain the operation of signal control processing. [Figure 18] This flowchart shows another example of signal control processing. [Modes for carrying out the invention]

[0022] The present invention will be described below through embodiments of the invention, but these embodiments are not intended to limit the invention as defined in the claims. Furthermore, not all combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0023] [First Embodiment] Figure 1 schematically illustrates the hazard prediction capability of the AI ​​for ultra-high-performance autonomous driving according to this embodiment. In this embodiment, information from multiple types of sensors is converted into AI data and stored in the cloud. The AI ​​predicts and determines the best mix of situations every nanosecond and optimizes vehicle operation.

[0024] Figure 2 schematically shows the Central Brain in the ultra-high-performance autonomous driving according to this embodiment. The Central Brain is an example of a control device that controls a Level 6 autonomous vehicle.

[0025] Level 6 represents autonomous driving, and is a higher level than Level 5, which represents fully autonomous driving. Although Level 5 represents fully autonomous driving, it is equivalent to human driving, and there is still a probability of accidents occurring. Level 6 represents a higher level than Level 5, and is a level where the probability of accidents is lower than that of Level 5.

[0026] Examples of sensors mounted on the vehicle in this embodiment include radar, LiDAR, high-resolution, telephoto, ultra-wide-angle, 360-degree, and high-performance cameras, vision recognition, subtle sound, ultrasound, vibration, infrared, ultraviolet, electromagnetic waves, temperature, humidity, spot AI weather forecasting, high-precision multi-channel GPS, low-altitude satellite information, and long-tail incident AI data. Long-tail incident AI data refers to trip data from vehicles with Level 5 implemented.

[0027] Sensor information collected from multiple types of sensors includes weight center of gravity shift, road material detection, outside temperature detection, outside humidity detection, up-down, side-to-side, and diagonal inclination angle detection of slopes, road freezing conditions, moisture content detection, tire material, wear status, air pressure detection, road width, presence or absence of no-passing zones, oncoming vehicles, vehicle type information of vehicles in front and behind, cruising status of those vehicles, and surrounding conditions (birds, animals, soccer balls, accident vehicles, earthquakes, fires, wind, typhoons, heavy rain, light rain, blizzards, fog, etc.). In this embodiment, these detections are performed every nanosecond.

[0028] In this embodiment, Central Brain may use this information to match the weather forecast with the highest accuracy rate for the entire road plus the smallest spot determined by AI. Central Brain may also use this information to match the location information of other vehicles. Furthermore, Central Brain may use this information to match the best estimated vehicle type (matching remaining fuel and speed at nanosecond intervals for that route). Central Brain may also use this information to match the mood of the passengers, such as the music they are listening to. Finally, Central Brain may use this information to perform instantaneous condition reconfiguration based on the desired mood.

[0029] The Central Brain could, for example, upload AI data to the cloud when a vehicle is charging. It could also form a Data Lake, where AI analyzes the data and uploads it in a constantly updated state.

[0030] As shown in Figure 3, while sensors mounted on a vehicle can detect objects such as other vehicles at long distances along a straight line in the direction of travel, there are blind spots at intersections and other locations where the sensors cannot detect objects. In the example in Figure 3, the solid rectangle enclosed by the dashed-dotted rectangle represents the vehicle on which the sensor is mounted, and the dashed-dotted arrow indicates the direction of travel of that vehicle. The shaded area represents the blind spot where the sensors mounted on that vehicle cannot detect objects.

[0031] In this case, the existence of blind spots for the vehicle increases the risk of traffic accidents.

[0032] Therefore, as shown in Figure 4, in this embodiment, sensors 110 capable of communicating with the Central Brain of a Level 6 autonomous vehicle are installed on all traffic lights 100 in the city. Examples of sensors 110 include radar, LiDAR, and high-resolution, telephoto, ultra-wide-angle, 360-degree, high-performance digital cameras. In the example in Figure 4, the sensors 110 are installed on top of the traffic lights 100, but the installation location of the sensors 110 is not limited to the top of the traffic lights 100. The sensors 110 may be installed on the side of the traffic lights 100, or on the pole portion of the traffic lights 100.

[0033] At each traffic light 100, the sensor 110 collects information detected in areas that are blind spots for the autonomous vehicle, and transmits road condition information to the Level 6 autonomous vehicle via wireless communication.

[0034] Central Brain acquires multiple pieces of information detected by sensors 110 installed on traffic signals 100, and uses the acquired information and AI to control the vehicle.

[0035] Central Brain can utilize both software and hardware approaches to optimize vehicle traffic. On the software side, Central Brain uses AI to best mix multiple pieces of information detected by sensors 110 installed on traffic signals 100, cloud-stored information, and vehicle sensor information. The AI ​​makes decisions every nanosecond to achieve autonomous driving that meets passenger needs. On the hardware side, the vehicle micro-controls the motor's rotation output every 1 / 1 billion second (nanosecond). The vehicle is equipped with electricity and motors capable of communicating and controlling at nanosecond intervals. According to Central Brain, because the AI ​​predicts crises, perfect stops are possible without the need for brakes or spilling a cup of water. Furthermore, power consumption is low, and there is no brake friction.

[0036] Figure 5 schematically shows the Perfect Speed ​​Control realized by the Central Brain control according to this embodiment. The principle shown in Figure 5 is an indicator for calculating the braking distance of a vehicle, and it is controlled by this basic equation. In the system according to this embodiment, since there is ultra-high-performance input data, it is possible to calculate with a clean bell curve.

[0037] Figure 6 schematically shows the Perfect Bell Curves realized by control by the Central Brain according to this embodiment.

[0038] A computing speed of 1 million TOPS can be achieved when realizing ultra-high-performance autonomous driving.

[0039] As described above, in this embodiment, the Central Brain may implement Perfect Cruise Control. The Central Brain may perform control according to the wishes of the occupants riding in the vehicle. Examples of occupant wishes include "shortest time," "longest battery life," "avoid motion sickness as much as possible," "feel the most G-force (safely)," "a mix of the above to feel the scenery the most," "feel a different scenery than last time," "for example, retrace memories of a road I traveled with someone many years ago," "avoid the probability of an accident as much as possible," etc. The Central Brain consults with the passengers about various other conditions, and then performs a perfect mix with the vehicle by selecting the above conditions every nanosecond, based on the number of passengers, weight, position, weight center of gravity shift (calculated every nanosecond), detection of road material every nanosecond, detection of outside air temperature every nanosecond, detection of outside air humidity every nanosecond, and total every nanosecond.

[0040] The Central Brain may consider and implement the following: "the incline of the road (up, down, sideways, and diagonally)", "matching with the most accurate weather forecast for the entire route + for each smallest spot determined by AI", "matching with the location information of other cars every nanosecond", "matching with the best estimated vehicle type (matching remaining fuel and speed every nanosecond on that route)", "matching with the mood of the passengers, such as the music they are listening to", "instantaneous readjustment of conditions when the desired mood changes", "estimation of the optimal mix of road freezing conditions, moisture content, wear of each tire material (4, 2, 8, 16, etc.), air pressure, and remaining road conditions every nanosecond", "the lane width, angle, and whether it is a no-passing lane at any given time", "vehicle types in the oncoming lane and the lanes ahead and behind, and their cruising status (every nanosecond)", and "the best mix of all other conditions".

[0041] The ideal position within each lane, rather than the center, varies depending on the speed, angle, and road conditions at the time. For example, it involves matching the best probability inferences for things like flying birds, animals, oncoming cars, flying soccer balls, children, accident vehicles, earthquakes, fires, wind, typhoons, heavy rain, light rain, blizzards, fog, and other nanosecond-by-nanosecond effects.

[0042] These are then perfectly matched using the capabilities of the current version of Central Brain and the latest updated information accumulated in the Brain Cloud up to that point.

[0043] This could be defined as perfect cruising in ultra-high-performance autonomous driving. To achieve this, ultra-high-performance autonomous driving requires 1 million TOPS of the best battery power management and AI-synchronized burst chilling temperature control at that moment.

[0044] Figures 7 to 13 are schematic diagrams of the Perfect Cruising.

[0045] As described above, according to this embodiment, sensors 110 capable of communicating with the Central Brain of a Level 6 autonomous vehicle are installed at all traffic lights 100 in the city. The Central Brain of the Level 6 autonomous vehicle can acquire information about blind spots from the sensors 110.

[0046] Therefore, it is possible to obtain information on blind spots that cannot be detected by autonomous vehicles, thereby reducing the risk of traffic accidents. Autonomous vehicles will be able to enter intersections even on red lights and operate at high speeds with precision, potentially increasing the overall traffic volume of a city tenfold or more. As a result, GDP will increase significantly.

[0047] Figure 14 schematically shows an example of the hardware configuration of a computer 1200 that functions as a Central Brain, which is an example of a control device. A program installed on the computer 1200 can cause the computer 1200 to function as one or more "parts" of the apparatus according to this embodiment, or to cause the computer 1200 to execute operations associated with the apparatus according to this embodiment or such one or more "parts", and / or to cause the computer 1200 to execute a process or a stage of such process according to this embodiment. Such a program may be executed by the CPU 1212 to cause the computer 1200 to execute specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0048] The computer 1200 according to this embodiment includes a CPU 1212, RAM 1214, and a graphics controller 1216, which are interconnected by a host controller 1210. The computer 1200 also includes input / output units such as a communication interface 1222, a storage device 1224, a DVD drive, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The DVD drive may be a DVD-ROM drive and a DVD-RAM drive, etc. The storage device 1224 may be a hard disk drive and a solid-state drive, etc. The computer 1200 also includes legacy input / output units such as a ROM 1230 and a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0049] The CPU 1212 operates according to the programs stored in the ROM 1230 and RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires the image data generated by the CPU 1212 and stores it in the frame buffer provided in RAM 1214 or within itself, so that the image data is displayed on the display device 1218.

[0050] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive reads programs or data from a DVD-ROM or the like and provides them to the storage device 1224. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0051] The ROM 1230 stores boot programs and / or hardware-dependent programs of the computer 1200, which are executed by the computer 1200 upon activation. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via USB ports, parallel ports, serial ports, keyboard ports, mouse ports, etc.

[0052] The program is provided on a computer-readable storage medium such as a DVD-ROM or IC card. The program is read from the computer-readable storage medium and installed on a storage device 1224, RAM 1214, or ROM 1230, which are examples of computer-readable storage media, and executed by the CPU 1212. The information processing described within these programs is read by the computer 1200, resulting in coordination between the program and the various types of hardware resources described above. The apparatus or method may be configured to realize the operation or processing of information in accordance with the use of the computer 1200.

[0053] For example, when communication is performed between a computer 1200 and an external device, the CPU 1212 may execute a communication program loaded into RAM 1214 and, based on the processing described in the communication program, instruct the communication interface 1222 to perform communication processing. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer area provided in a recording medium such as RAM 1214, storage device 1224, DVD-ROM, or IC card, transmits the read transmission data to the network, or writes received data received from the network to a reception buffer area provided on the recording medium.

[0054] Furthermore, the CPU 1212 may read all or necessary parts of a file or database stored on an external recording medium such as a storage device 1224, a DVD drive (DVD-ROM), or an IC card into the RAM 1214, and perform various types of processing on the data in the RAM 1214. The CPU 1212 may then write the processed data back to the external recording medium.

[0055] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and subjected to information processing. The CPU 1212 may perform various types of processing on the data read from RAM 1214, including various types of operations, information processing, conditional judgments, conditional branching, unconditional branching, information retrieval / replacement, etc., as described throughout this disclosure and specified by the program instruction sequence, and write the results back to RAM 1214. The CPU 1212 may also retrieve information in files, databases, etc., within the recording medium. For example, if multiple entries are stored in the recording medium, each having an attribute value of a first attribute associated with an attribute value of a second attribute, the CPU 1212 may search among the multiple entries for an entry that matches the specified condition for the attribute value of the first attribute, read the attribute value of the second attribute stored in that entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies the predetermined condition.

[0056] The program or software module described above may be stored on or near the computer 1200 in a computer-readable storage medium. Alternatively, a recording medium such as a hard disk or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the program to the computer 1200 via the network.

[0057] In this embodiment, blocks in the flowchart and block diagram may represent a stage in a process in which an operation is performed or a "part" of a device that has the role of performing an operation. A particular stage and "part" may be implemented by a dedicated circuit, a programmable circuit supplied with computer-readable instructions stored on a computer-readable storage medium, and / or a processor supplied with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuit may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. The programmable circuit may include reconfigurable hardware circuits, such as field-programmable gate arrays (FPGAs) and programmable logic arrays (PLAs), which include logical AND, logical OR, exclusive OR, negated AND, negated OR, and other logical operations, flip-flops, registers, and memory elements.

[0058] A computer-readable storage medium may include any tangible device capable of storing instructions to be executed by a suitable device, and as a result, a computer-readable storage medium having instructions stored therein will comprise a product that includes instructions that can be executed to create means for performing operations specified in a flowchart or block diagram. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital multipurpose disc (DVD), Blu-ray® disc, memory stick, integrated circuit card, etc.

[0059] Computer-readable instructions may include assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, Java®, C++, and traditional procedural programming languages ​​such as the C programming language or similar programming languages.

[0060] Computer-readable instructions may be provided to a general-purpose computer, a special-purpose computer, or a programmable circuit, either locally or via a wide area network (WAN) such as a local area network (LAN) or the internet, so that the computer-readable instructions may be executed by the processor or programmable circuit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, in order to generate means for performing operations specified in a flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, and the like.

[0061] [Second Embodiment] Next, a second embodiment of this disclosure will be described. Parts identical to those in the first embodiment will be denoted by the same reference numerals, and their descriptions will be omitted.

[0062] Figure 15 shows a traffic signal system 10 according to the second embodiment. The traffic signal system 10 includes a plurality of traffic signal devices 12 installed at each road intersection, a plurality of autonomous vehicles 16, and a traffic signal control device 22. The traffic signal device 12 includes the traffic signal 100 and sensor 110 described in the first embodiment, and a wireless communication unit 14 for wireless communication with the traffic signal control device 22. In the second embodiment, the sensor 110 is capable of detecting traffic conditions such as when an emergency vehicle (e.g., a police vehicle, ambulance, fire truck, etc., driving with its siren on) is about to pass through an intersection where the traffic signal device 12 is installed.

[0063] The autonomous vehicle 16 includes a driving plan creation unit 18 and a wireless communication unit 20 for wireless communication with a signal control device 22. The driving plan creation unit 18 is realized by the Central Brain, as described in the first embodiment, executing a predetermined program. When a destination for the autonomous vehicle 16 is set, the driving plan creation unit 18 triggers the process of creating a driving plan that subdivides the route to the set destination into driving plans such as going straight or turning left or right at intersections, and also defines the scheduled execution time for each driving plan. The Central Brain controls the autonomous vehicle 16 to drive autonomously according to the driving plan created by the driving plan creation unit 18.

[0064] The signal control device 22 includes a CPU, memory such as ROM or RAM, a non-volatile storage unit such as an HDD or SSD, and a wireless communication unit 32. The storage unit stores a signal control program. The signal control device 22 functions as a first acquisition unit 24, a second acquisition unit 26, a determination unit 28, a control unit 30, and a cooperative control unit 31 when the CPU executes the signal control program, and performs the signal control processing described later (Figure 16). Note that the signal control device 22 is an example of a signal control device in this disclosure.

[0065] The first acquisition unit 24 acquires traffic conditions around the intersection from sensors 110 installed around the intersection. The second acquisition unit 26 acquires the driving plan of the autonomous vehicle 16 that is scheduled to pass through the intersection. The determination unit 28 determines, based on the traffic conditions around the intersection acquired by the first acquisition unit 24, whether or not a delay will occur in the driving plan of the autonomous vehicle 16 when it passes through the intersection.

[0066] If the determination unit 28 determines that a delay will occur in the driving plan of the autonomous vehicle 16 when it passes through an intersection, the control unit 30 controls the traffic lights 100 at the intersection so as to suppress the delay in the driving plan of the autonomous vehicle 16 when it passes through the intersection. If the determination unit 28 determines that a delay will occur in the driving plan of the autonomous vehicle 16, the cooperative control unit 31 controls the traffic lights 100 at each of the multiple intersections that the autonomous vehicle 16 is scheduled to pass through sequentially so as to suppress the delay in the driving plan of the autonomous vehicle 16.

[0067] Next, the operation of the second embodiment will be described. In the second embodiment, the signal control device 22 constantly monitors the position and speed of each autonomous vehicle 16 by periodically communicating with each autonomous vehicle 16 traveling on the road. The signal control device 22 then triggers the signal control process shown in Figure 16 when any of the autonomous vehicles 16 approaches within a predetermined distance from an intersection where a signal device 12 is installed (hereinafter referred to as the controlled intersection).

[0068] In step 50 of the signal control processing, the first acquisition unit 24 of the signal control device 22 acquires traffic conditions at the intersection to be controlled, such as whether an emergency vehicle is about to pass through the intersection to be controlled, from the sensor 110.

[0069] Furthermore, in step 52, the second acquisition unit 26 acquires a driving plan from the autonomous vehicle 16 that is scheduled to pass through the controlled intersection. The driving plan acquired by the second acquisition unit 26 from the autonomous vehicle 16 includes information on the autonomous vehicle 16's planned movement at the controlled intersection (straight ahead / left turn / right turn) and the planned execution time of said driving (planned time of passing through the controlled intersection).

[0070] As an example, Figure 17 shows an example of a driving plan for the autonomous vehicle 16 acquired by the second acquisition unit 26, labeled as the "initial driving plan." This "initial driving plan" is designed to allow the vehicle to pass through the intersection to be controlled without waiting at the traffic light while the traffic light 100 at the intersection is green.

[0071] In step 54, the determination unit 28 calculates the time when the autonomous vehicle 16 will pass through the controlled intersection based on the traffic conditions at the controlled intersection acquired by the first acquisition unit 24 in step 50. In step 56, the determination unit 28 determines whether the intersection passage time calculated in step 54 is delayed by a predetermined time or more from the autonomous vehicle 16's driving plan (scheduled time of passage through the controlled intersection).

[0072] For example, if there are no emergency vehicles attempting to pass through the intersection to be controlled, the time difference between the time calculated in step 54 and the driving plan of the automated vehicle 16 (scheduled time of passing through the intersection to be controlled) will be less than a predetermined time, and the determination in step 56 will be rejected. In this case, step 58 is skipped and the signal control process ends.

[0073] On the other hand, if there is an emergency vehicle attempting to pass through the intersection to be controlled, for example, as shown in Figure 17 labeled "Actual planned travel time estimated from surrounding traffic conditions," the time required to pass through the intersection to be controlled will be increased by the time spent waiting for the emergency vehicle to pass or waiting at traffic lights. As a result, as shown in Figure 17 labeled "Delay t1," the time calculated in step 54 will be delayed by a predetermined amount of time or more compared to the travel plan of the autonomous vehicle 16 (scheduled time of passing through the intersection to be controlled), thus confirming the judgment in step 56 and proceeding to step 58.

[0074] In step 58, the control unit 30 controls the traffic lights 100 at the controlled intersection so that they remain green while the autonomous vehicle 16 passes through the controlled intersection (see also "Traffic light color after control" in Figure 17), and then terminates the signal control process. As a result, as shown in Figure 17 as an example labeled "Planned driving under the traffic light color after control," the time required to pass through the controlled intersection is reduced by the amount of time spent waiting at the traffic lights (see also "Delay suppression (t2)"), and delays in the autonomous vehicle 16's driving plan are suppressed.

[0075] Next, with reference to Figure 18, another example of signal control processing performed by the signal control device 22 will be described. The signal control processing shown in Figure 18 proceeds to step 60 after the processing in step 58. In step 60, the cooperative control unit 31 determines whether the delay in the driving plan of the automated vehicle 16 has been eliminated as a result of the control of the traffic light 100 at the intersection to be controlled in step 58. If the determination in step 60 is affirmative, the signal control processing is terminated.

[0076] On the other hand, if the determination in step 60 is negative, the process proceeds to step 62. In step 62, the cooperative control unit 31 controls the traffic light 100 at the next intersection so that it remains green while the autonomous vehicle 16 passes through the next intersection. After the processing in step 62 is completed, the process returns to step 60, and steps 60 and 62 are repeated until the determination in step 60 is affirmed. In this way, the traffic lights 100 at multiple intersections that the autonomous vehicle 16 passes through sequentially are controlled in a coordinated manner so as to eliminate any delays in the autonomous vehicle 16's travel plan.

[0077] As described above, in the second embodiment, the first acquisition unit 24 of the signal control device 22 acquires traffic conditions around the intersection to be controlled from sensors 110 installed around the intersection to be controlled, and the second acquisition unit 26 acquires the driving plan of the autonomous vehicle 16 that is scheduled to pass through the intersection to be controlled. The determination unit 28 determines, based on the traffic conditions acquired by the first acquisition unit 24, whether or not a delay will occur in the driving plan of the autonomous vehicle 16 when it passes through the intersection to be controlled. If the determination unit 28 determines that a delay will occur, the control unit 30 controls the traffic lights 100 at the intersection to be controlled so as to suppress the delay. This makes it possible to suppress delays in the driving plan of the autonomous vehicle 16, and to prevent a large load, such as the recalculation of the driving plan, from being placed on the onboard computer (Central Brain) that performs autonomous driving control, etc., during driving.

[0078] Furthermore, in the second embodiment, when the determination unit 28 determines that the delay has occurred, the control unit 30 controls the traffic lights 100 at the controlled intersection so that the traffic lights 100 at the controlled intersection remain green while the autonomous vehicle 16 is passing through the controlled intersection. This makes it possible to ensure the safety of the autonomous vehicle 16 as it passes through the controlled intersection while preventing the traffic lights at the controlled intersection from remaining green for an unnecessarily long period of time, compared to the case where control is performed to extend the time the traffic lights 100 at the controlled intersection remain green for a certain period of time.

[0079] Furthermore, in the second embodiment, if the determination unit 28 determines that the delay will occur, the cooperative control unit 31 controls the traffic lights 100 at each of the multiple intersections that the autonomous vehicle 16 is scheduled to pass through sequentially, so as to suppress the delay (Figure 18). This makes it possible to eliminate delays in the autonomous vehicle 16's travel plan while the autonomous vehicle 16 passes through the multiple intersections sequentially.

[0080] In the second embodiment, the process of controlling the traffic lights 100 at the target intersection when a delay occurs in the driving plan of an autonomous vehicle 16 was described in a manner that applies to all autonomous vehicles 16 passing through the target intersection. However, this disclosure is not limited thereto. For example, an urgency level may be set in advance for each autonomous vehicle 16, and the process of controlling the traffic lights 100 at the target intersection when a delay occurs in the driving plan of an autonomous vehicle 16 may be applied only to autonomous vehicles 16 whose urgency level is above a predetermined value. This allows for prioritizing the suppression of delays in the driving plan of, for example, an autonomous vehicle 16 transporting a sick person by setting its urgency level above a predetermined value. Furthermore, by suppressing the number of times the traffic lights 100 at the target intersection are controlled, the number of other vehicles besides autonomous vehicles 16 whose driving may be affected by the control of the traffic lights 100 at the target intersection can also be suppressed.

[0081] Furthermore, in the second embodiment, an example of a traffic situation in which a delay in the driving plan of the autonomous vehicle 16 occurs was described as the case in which the autonomous vehicle 16 encounters an emergency vehicle at an intersection. However, this disclosure is not limited to this, and other examples of traffic situations in which a delay in the driving plan of the autonomous vehicle 16 occurs include cases in which there are pedestrians that interfere with the autonomous vehicle 16 when the autonomous vehicle 16 is turning right or left at an intersection.

[0082] Furthermore, while the second embodiment describes an embodiment in which one signal control device 22 is provided for multiple signal devices 12, the disclosure is not limited thereto. For example, a signal control device 22 equipped with each functional unit (first acquisition unit 24, second acquisition unit 26, determination unit 28, and control unit 30) excluding the cooperative control unit 31 may be provided for each intersection corresponding to each signal device 12. In this embodiment, the devices (signal devices 12 and signal control device 22) provided for each intersection are an example of a signal system according to the disclosure. Also, in this embodiment, when performing cooperative control of multiple traffic lights 100, one cooperative control device that functions as a cooperative control unit 31 may be provided for multiple signal systems (signal devices 12 and signal control device 22). The signal system 10 in this embodiment with the cooperative control device is an example of a signal system according to the disclosure.

[0083] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications or improvements can be made to the above embodiments. It will be clear from the claims that such modified or improved forms may also be included in the technical scope of the present invention.

[0084] It should be noted that the execution order of operations, procedures, steps, and stages in the apparatus, systems, programs, and methods shown in the claims, specifications, and drawings is not explicitly stated as "before" or "prior to," and that these can be implemented in any order unless the output of a previous process is used in a later process. Even if the operation flow in the claims, specifications, and drawings is described using phrases such as "first," and "next," for convenience, this does not mean that it is essential to perform the operations in that order. [Explanation of Symbols]

[0085] 10 Signal control system, 12 Signal device, 16 Autonomous vehicle, 18 Driving plan creation unit, 22 Signal control device, 24 First acquisition unit, 26 Second acquisition unit, 28 Judgment unit, 30 Control unit, 31 Coordination control unit, 100 Traffic light, 110 Sensor

Claims

1. A first acquisition unit acquires information from sensors installed around the intersection, including at least one of the following regarding traffic conditions around the intersection: whether or not an emergency vehicle is attempting to pass through the intersection, and whether or not there are pedestrians who may interfere with the autonomous vehicle when turning right or left at the intersection. A second acquisition unit acquires the driving plan of the autonomous vehicle that is scheduled to pass through the aforementioned intersection, Based on the traffic conditions acquired by the first acquisition unit, a determination unit determines whether or not the scheduled time of passage of the autonomous vehicle through the intersection will be delayed compared to the autonomous vehicle's travel plan. When the determination unit determines that the delay occurs, a control unit controls the traffic signals at the intersection in such a way that the delay is suppressed. A signal control device that includes a signal control device.

2. The signal control device according to claim 1, wherein the control unit controls the traffic lights at the intersection so that they remain green while the autonomous vehicle is passing through the intersection when the determination unit determines that the delay has occurred.

3. The signal control device according to claim 1, wherein the control unit controls the traffic signals at the intersection in such a way as to suppress the aforementioned delay, and the autonomous vehicle is an autonomous vehicle whose urgency level is set in advance to a predetermined value or higher.

4. The signal control device according to claim 1, further comprising: if the determination unit determines that the delay has occurred, it determines whether the delay has been resolved by controlling the traffic signals at the intersection, and if it determines that the delay has not been resolved, it controls the traffic signals at the next intersection so that the next intersection remains green while the autonomous vehicle is passing through the next intersection it is scheduled to pass through.

5. A signal control device according to any one of claims 1 to 3, The aforementioned signal light, A traffic signal system that includes traffic signals and is installed at each intersection.

6. A traffic signal device according to claim 5, provided at each of multiple intersections, If the determination unit of any of the multiple traffic signal devices determines that the delay has occurred, the cooperative control unit determines whether the delay has been resolved by controlling the traffic signals at the intersection where any of the determination units are located, and if it determines that the delay has not been resolved, the cooperative control unit controls the traffic signals at the next intersection so that the next intersection remains green while the automated vehicle is passing through that next intersection. A traffic light system including a signal light system.

7. On the computer, Sensors installed around the intersection acquire information regarding the traffic conditions around the intersection, including at least one of whether an emergency vehicle is attempting to pass through the intersection and whether there are pedestrians who may interfere with the autonomous vehicle when turning right or left at the intersection, and also acquire the driving plan of the autonomous vehicle that is scheduled to pass through the intersection. Based on the acquired traffic conditions, it is determined whether the scheduled time of passage of the autonomous vehicle through the intersection will be delayed compared to the autonomous vehicle's travel plan. A signal control program for causing a process to be executed that includes controlling the traffic signals at the intersection in such a way that the delay is suppressed when it is determined that the aforementioned delay occurs.

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