Travel control system, agricultural machine and travel control method

Abstract

A travel control system, which is for an agricultural machine operating automatically while sensing the surrounding environment, comprises: a storage unit that stores a traveling history including the past traveling conditions of the agricultural machine and the position data of a candidate obstacle detected during traveling; and a processing unit that allows the agricultural machine to execute an avoidance operation to avoid the obstacle on the basis of sensor data obtained by sensing the surrounding environment and the traveling history.

Description

Travel control system, agricultural machine, and travel control method

[0001] The present disclosure relates to a cruise control system for an autonomously driven agricultural machine, an agricultural machine equipped with such a cruise control system, and a cruise control method.

[0002] Research and development is underway to automate agricultural machinery. For example, work vehicles such as tractors, combine harvesters, and rice transplanters that automatically navigate within fields using positioning systems such as the Global Navigation Satellite System (GNSS) have been put to practical use. Research and development is also underway on work vehicles that automatically navigate not only within fields but also outside of fields. Patent Document 1 discloses a system that automatically navigates an unmanned work vehicle between two fields separated by a road.

[0003] Japanese Patent Application Laid-Open No. 2021-029218

[0004] There is a need to control the operation of autonomous agricultural machinery so that it operates efficiently.

[0005] The present disclosure provides a travel control system that can control agricultural machinery to travel efficiently when driven autonomously, an agricultural machinery equipped with such a travel control system, and a travel control method that can control agricultural machinery to travel efficiently when driven autonomously.

[0006] A driving control system according to one aspect of the present disclosure is a driving control system for an agricultural machine that drives automatically while sensing the surrounding environment, and includes a storage device that stores a driving history including information on the agricultural machine's past driving conditions and the positions of potential obstacles detected while driving, and a processing device that causes the agricultural machine to perform evasive action to avoid obstacles based on sensor data obtained by sensing the surrounding environment and the driving history.

[0007] A driving control method according to one aspect of the present disclosure is a driving control method for an agricultural machine that drives automatically while sensing the surrounding environment, and includes storing a driving history including information on the agricultural machine's past driving conditions and the positions of potential obstacles detected during driving, and causing the agricultural machine to perform evasive action to avoid obstacles based on sensor data obtained by sensing the surrounding environment and the driving history.

[0008] A general or specific aspect of the present disclosure may be realized by an apparatus, a system, a method, an integrated circuit, a computer program, or a computer-readable non-transitory storage medium, or any combination thereof. The computer-readable storage medium may include a volatile storage medium or a non-volatile storage medium. An apparatus may be composed of multiple devices. When an apparatus is composed of two or more devices, the two or more devices may be located in a single device or may be located separately in two or more separate devices.

[0009] According to an embodiment of the present disclosure, there are provided a travel control system that can control agricultural machinery to travel efficiently when driven autonomously, an agricultural machinery equipped with such a travel control system, and a travel control method that can control agricultural machinery to travel efficiently when driven autonomously.

[0010] 1 is a diagram for explaining an overview of an agricultural management system according to an exemplary embodiment of the present disclosure.

[0022] FIG. 1 is a side view schematically illustrating an example of a work vehicle and an implement coupled to the work vehicle.

[0023] FIG. 2 is a block diagram illustrating an example configuration of a work vehicle and an implement.

[0024] FIG. 3 is a conceptual diagram illustrating an example of a work vehicle that performs positioning using RTK-GNSS.

[0025] FIG. 4 is a diagram illustrating an example of an operation terminal and an operation switch group provided inside the cabin.

[0026] FIG. 5 is a block diagram illustrating an example of the hardware configuration of a management device and a terminal device.

[0027] FIG. 6 is a diagram illustrating an example of a work vehicle that automatically travels along a target route in a field.

[0028] FIG. 7 is a flowchart illustrating an example of steering control operation during autonomous driving.

[0029] FIG. 8 is a diagram illustrating an example of a work vehicle traveling along a target route P.

[0029] FIG. 9 is a diagram illustrating an example of a work vehicle that is shifted to the right from the target route P.

[0030] FIG. 10 is a diagram illustrating an example of a work vehicle facing in an inclined direction with respect to the target route P.

[0031] FIG. 11 is a diagram illustrating an example of a situation in which multiple work vehicles are automatically traveling on roads inside and outside of a field.

[0032] FIG. 12 is a diagram illustrating an example of a global route and a local route that are generated in an environment where obstacles are present. 1 is a flowchart showing a method for route planning and cruise control. FIG. 2 is a diagram showing a schematic example of an area that can be sensed by a LiDAR sensor carried by a work vehicle. FIG. 3 is a diagram showing a schematic example of an area that can be sensed by a camera carried by a work vehicle. FIG. 4 is a flowchart showing an example of a procedure for a work vehicle to travel using a cruise control system. FIG. 5 is a flowchart showing an example of a procedure for a work vehicle to travel using a cruise control system. FIG. 6 is a flowchart showing an example of a procedure for a work vehicle to travel using a cruise control system.

[0011] (Definition of Terms) In this disclosure, "agricultural machinery" refers to machinery used for agricultural purposes. Examples of agricultural machinery include tractors, harvesters, rice transplanters, riding cultivators, vegetable transplanters, mowers, seed sowing machines, fertilizer applicators, and agricultural mobile robots. Not only can a work vehicle such as a tractor function alone as an "agricultural machinery," but the entire work vehicle and an implement attached to or towed by the work vehicle can also function as a single "agricultural machinery." Agricultural machinery performs agricultural work on the ground in a field, such as plowing, sowing, pest control, fertilizing, planting crops, or harvesting. These agricultural works are sometimes referred to as "ground work" or simply "work." Traveling while performing agricultural work by a vehicle-type agricultural machine is sometimes referred to as "work travel."

[0012] "Autonomous driving" refers to controlling the movement of an agricultural machine through the action of a control device, without manual operation by a driver. Agricultural machines that perform autonomous driving are sometimes called "autonomous agricultural machines" or "robotic agricultural machines." During autonomous driving, not only the movement of the agricultural machine but also the agricultural work operations (e.g., the operation of the implement) may be automatically controlled. When the agricultural machine is a vehicle-type machine, the traveling of the agricultural machine through autonomous driving is referred to as "autonomous driving." The control device may control at least one of the following operations required for the movement of the agricultural machine: steering, adjusting the movement speed, and starting and stopping the movement. When controlling a work vehicle equipped with an implement, the control device may control operations such as raising and lowering the implement, starting and stopping the operation of the implement, etc. Autonomous driving movement may include not only movement of the agricultural machine toward a destination along a predetermined route, but also movement of the agricultural machine following a tracking target. An autonomously driving agricultural machine may move partially based on user instructions. Furthermore, an autonomously driving agricultural machine may operate in a manual driving mode, in addition to an autonomous driving mode, in which the agricultural machine moves through manual operation by the driver. Steering an agricultural machine by the action of a control device, without manual operation, is called "automatic steering." Part or all of the control device may be external to the agricultural machine. Communication of control signals, commands, data, etc. may take place between the agricultural machine and a control device external to the agricultural machine. An agricultural machine that performs automatic driving may move autonomously while sensing the surrounding environment, without human involvement in controlling the movement of the agricultural machine. An agricultural machine capable of autonomous movement can travel unmanned within or outside a field (e.g., on a road). During autonomous movement, it may detect obstacles and perform obstacle avoidance operations.

[0013] A "work plan" is data that schedules one or more agricultural tasks to be performed by an agricultural machine. The work plan may include, for example, information indicating the order of agricultural tasks to be performed by the agricultural machine and the field on which each task will be performed. The work plan may also include information on the scheduled date and time for each task. A work plan that includes information on the scheduled date and time for each task is particularly referred to as a "work schedule" or simply a "schedule." The work schedule may include information on the scheduled start time and / or scheduled end time for each task to be performed on each work day. The work plan or work schedule may include information for each task, such as the content of the task, the implements to be used, and / or the type and amount of agricultural materials to be used. Here, "agricultural materials" refers to materials used in agricultural tasks performed by an agricultural machine. Agricultural materials may also be simply referred to as "materials." Agricultural materials may include materials consumed in agricultural tasks, such as pesticides, fertilizers, seeds, or seedlings. The work plan may be created by a processing device that communicates with the agricultural machine to manage the agricultural task, or a processing device mounted on the agricultural machine. The processing device can create a work plan based on information input by a user (such as a farm manager or farm worker) by operating a terminal device, for example. In this specification, a processing device that communicates with agricultural machines and manages agricultural work is referred to as a "management device." The management device may manage the agricultural work of multiple agricultural machines. In this case, the management device may create a work plan that includes information about each agricultural work to be performed by each of the multiple agricultural machines. The work plan can be downloaded by each agricultural machine and stored in a storage device. Each agricultural machine can automatically head to a field and perform the scheduled agricultural work in accordance with the work plan.

[0014] An "environmental map" is data that represents the positions or areas of objects in the environment in which the agricultural machine moves using a specified coordinate system. An environmental map may be simply referred to as a "map" or "map data." The coordinate system that defines the environmental map may be, for example, a world coordinate system such as a geographic coordinate system fixed relative to the Earth. An environmental map may also include information other than the positions of objects in the environment (for example, attribute information and other information). Environmental maps include maps in various formats, such as point cloud maps or grid maps. Data for local maps or partial maps that are generated or processed in the process of constructing an environmental map are also referred to as a "map" or "map data."

[0015] "Farm road" means a road that is primarily used for agricultural purposes. Farm roads are not limited to roads paved with asphalt, but also include unpaved roads covered with dirt or gravel. Farm roads include roads (including private roads) that are exclusively accessible to vehicle-type agricultural machinery (e.g., work vehicles such as tractors) and roads that are also accessible to general vehicles (passenger cars, trucks, buses, etc.). Work vehicles may automatically travel on public roads in addition to farm roads. Public roads are roads that have been developed for the traffic of general vehicles.

[0016] "Feature" means something that exists on the ground. Examples of features include waterways, grass, trees, roads, fields, ditches, rivers, bridges, forests, mountains, rocks, buildings, and railroad tracks. Things that do not exist in the real world, such as boundaries, place names, building names, field names, and road names, are not included in the "feature" in this disclosure.

[0017] "GNSS satellite" refers to an artificial satellite in the global navigation satellite system (GNSS). GNSS is a general term for satellite positioning systems such as GPS (Global Positioning System), QZSS (Quasi-Zenith Satellite System), GLONASS, Galileo, and BeiDou. GNSS satellites are satellites in these positioning systems. Signals transmitted from GNSS satellites are called "satellite signals." A "GNSS receiver" is a device that receives radio waves transmitted from multiple satellites in the GNSS and performs positioning based on signals superimposed on the radio waves. "GNSS data" is data output from a GNSS receiver. GNSS data can be generated in a predetermined format, such as the NMEA-0183 format. GNSS data can include, for example, information indicating the reception status of satellite signals received from individual satellites. For example, the GNSS data may include values ​​indicating the identification number, elevation angle, azimuth angle, and reception strength of each satellite from which a satellite signal is received. The reception strength is a numerical value indicating the strength of a received satellite signal. The reception strength may be expressed, for example, as a value such as the carrier-to-noise density ratio (C / N0). The GNSS data may include position information of the GNSS receiver or agricultural machine calculated based on multiple received satellite signals. The position information may be represented, for example, by latitude, longitude, and height above mean sea level. The GNSS data may further include information indicating the reliability of the position information.

[0018] "Satellite signals can be received normally" means that satellite signals can be received stably enough that the reliability of positioning is not significantly reduced. The inability to receive satellite signals normally is sometimes referred to as "satellite signal reception interference." "Satellite signal reception interference" refers to a state in which the reliability of positioning is reduced compared to normal due to deterioration of satellite signal reception conditions. Reception interference can occur, for example, when the number of detected satellites is small (e.g., three or fewer), when the reception strength of each satellite signal is low, or when multipath occurs. Whether reception interference is occurring can be determined, for example, based on information about the satellites contained in the GNSS data. For example, the presence or absence of reception interference can be determined based on the reception strength value for each satellite contained in the GNSS data or the DOP (Dilution of Precision) value, which indicates the satellite positioning status.

[0019] A "global path" refers to data on a path connecting a starting point to a destination point when an agricultural machine moves automatically, generated by a processing device that performs path planning. Generating a global path is referred to as global path planning or global path design. In the following description, the global path is also referred to as a "target path" or simply as a "path." A global path can be defined, for example, by the coordinate values ​​of multiple points through which the agricultural machine must pass. A point through which the agricultural machine must pass is referred to as a "waypoint," and a line segment connecting adjacent waypoints is referred to as a "link."

[0020] A "local path" refers to a local path that can avoid obstacles and is generated sequentially when an agricultural machine automatically moves along a global path. Generating a local path is called local path planning or local path design. A local path is generated sequentially based on data acquired by one or more sensing devices equipped on the agricultural machine while the agricultural machine is moving. A local path may be defined by multiple waypoints along a portion of the global path. However, if an obstacle exists near the global path, waypoints may be set to bypass the obstacle. The length of the links between waypoints on a local path is shorter than the length of the links between waypoints on the global path. The device that generates the local path may be the same as or different from the device that generates the global path. For example, a management device that manages farm work by the agricultural machine may generate the global path, and a control device installed on the agricultural machine may generate the local path. In this case, the combination of the management device and the control device functions as a "processing device" that performs path planning. The agricultural machine's controller may function as a processor for both global and local path planning.

[0021] A "storage location" is a location provided for storing agricultural machinery. The storage location may be, for example, a location managed by a user of the agricultural machinery, or a location jointly operated by multiple users. The storage location may be, for example, a location set aside for storing agricultural machinery, such as a warehouse, barn, or parking lot at the home or business of a user (such as a farmer). The location of the storage location may be registered in advance and recorded in a storage device.

[0022] A "waiting location" is a location provided for an agricultural machine to wait while not performing agricultural work. One or more waiting locations may be provided in an environment in which the agricultural machine performs autonomous driving. The storage location described above is an example of a waiting location. A waiting location may be a location jointly managed or used by multiple users. A waiting location may be, for example, a warehouse, garage, barn, parking lot, or other facility. A waiting location may be a warehouse, barn, garage, or parking lot at the home or business of a farmer who is different from the user of the agricultural machine. Multiple waiting locations may be scattered throughout the environment in which the agricultural machine moves. Work such as replacement or maintenance of agricultural machine parts or implements, or replenishment of materials, may be performed at a waiting location. In this case, parts, tools, or materials required for those work may be stored at the waiting location.

[0023] (Embodiments) Hereinafter, embodiments of the present disclosure will be described. However, more detailed descriptions than necessary may be omitted. For example, detailed descriptions of already well-known matters and redundant descriptions of substantially identical configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. Note that the inventors provide the accompanying drawings and the following description to enable those skilled in the art to fully understand the present disclosure, and do not intend for them to limit the subject matter described in the claims. In the following description, components having the same or similar functions are designated by the same reference numerals.

[0024] A general or specific aspect of the present disclosure may be realized by an apparatus, a system, a method, an integrated circuit, a computer program, or a computer-readable non-transitory storage medium, or any combination thereof. The computer-readable storage medium may include a volatile storage medium or a non-volatile storage medium. An apparatus may be composed of multiple devices. When an apparatus is composed of two or more devices, the two or more devices may be located in a single device or may be located separately in two or more separate devices.

[0025] The following embodiments are examples, and the technology of the present disclosure is not limited to the following embodiments. For example, the numerical values, shapes, materials, steps, step order, display screen layout, etc. shown in the following embodiments are merely examples, and various modifications are possible as long as no technical contradiction occurs. Furthermore, one aspect can be combined with another aspect as long as no technical contradiction occurs.

[0026] The following mainly describes an embodiment in which the technology of the present disclosure is applied to a work vehicle such as a tractor, which is an example of agricultural machinery. The technology of the present disclosure is not limited to work vehicles such as tractors, but can also be applied to other types of agricultural machinery.

[0027] FIG. 1 is a diagram for explaining an overview of an agricultural management system according to an exemplary embodiment of the present disclosure. The agricultural management system shown in FIG. 1 includes a work vehicle 100, a terminal device 400, and a management device 600. The terminal device 400 is a computer used by a user to remotely monitor the work vehicle 100. The management device 600 is a computer managed by a business operator that operates the agricultural management system. The work vehicle 100, the terminal device 400, and the management device 600 can communicate with each other via a network 80. Although FIG. 1 illustrates one work vehicle 100, the agricultural management system may include multiple work vehicles or other agricultural machines.

[0028] The work vehicle 100 in this embodiment is a tractor. The work vehicle 100 can be fitted with an implement at either the rear or the front, or both. The work vehicle 100 can travel within a field while performing agricultural work according to the type of implement. The work vehicle 100 may also travel within or outside a field without an implement attached.

[0029] The work vehicle 100 has an automatic driving function. That is, the work vehicle 100 can travel by the operation of a control device, without manual operation. The control device in this embodiment is provided inside the work vehicle 100, and can control both the speed and steering of the work vehicle 100. The work vehicle 100 can travel automatically not only within a field, but also outside the field (for example, on a road).

[0030] The work vehicle 100 is equipped with devices used for positioning or self-location estimation, such as a GNSS receiver and a LiDAR sensor. The control device of the work vehicle 100 causes the work vehicle 100 to travel automatically based on the position of the work vehicle 100 and information on the target route generated by the management device 600. In addition to controlling the travel of the work vehicle 100, the control device also controls the operation of the implement. This allows the work vehicle 100 to perform agricultural work using the implement while traveling automatically within a field. Furthermore, the work vehicle 100 can automatically travel along a target route on roads outside the field (e.g., farm roads or public roads). When traveling automatically along roads outside the field, the work vehicle 100 travels while generating a local route along the target route that can avoid obstacles based on data output from sensing devices such as a camera or LiDAR sensor. Within the field, the work vehicle 100 may travel while generating a local route as described above, or may travel along a target route without generating a local route and stop if an obstacle is detected.

[0031] The management device 600 is a computer that manages agricultural work performed by the work vehicle 100. The management device 600 may be, for example, a server computer that centrally manages information about farm fields on the cloud and supports agriculture by utilizing data on the cloud. The management device 600 may, for example, create a work plan for the work vehicle 100 and generate a target route for the work vehicle 100 in accordance with the work plan. Alternatively, the management device 600 may generate the target route for the work vehicle 100 in response to a user's operation using the terminal device 400. Hereinafter, unless otherwise specified, the target route (i.e., global route) for the work vehicle 100 generated by the management device 600 will be simply referred to as a "route."

[0032] The management device 600 generates target routes within the field and outside the field using different methods. The management device 600 generates target routes within the field based on information about the field. For example, the management device 600 can generate target routes within the field based on various information, such as pre-registered field outlines, field area, the location of field entrances and exits, the width of the work vehicle 100, the width of the implement, the type of work being done, the type of crop being cultivated, the crop growing area, the crop growth conditions, or the spacing between crop rows or furrows. The management device 600 generates target routes within the field based on information input by the user using the terminal device 400 or another device, for example. The management device 600 generates routes within the field so as to cover, for example, the entire work area where work is to be done. On the other hand, the management device 600 generates target routes outside the field in accordance with a work plan or user instructions. For example, the management device 600 can generate a target route outside the field based on various information such as the order of farm work indicated in the work plan, the location of the field where each farm work will be performed, the location of the entrance and exit to the field, the scheduled start and end times of each farm work, attribute information of each road recorded on the map, road surface conditions, weather conditions, traffic conditions, etc. The management device 600 may also generate a target route based on information indicating a route or waypoints specified by the user by operating the terminal device 400, regardless of the work plan.

[0033] Management device 600 may further generate and edit an environmental map based on data collected by work vehicle 100 or other moving bodies using a sensing device such as a LiDAR sensor. Management device 600 transmits the generated work plan, target route, and environmental map data to work vehicle 100. Work vehicle 100 automatically moves and performs farm work based on this data.

[0034] The global route design and the generation (or editing) of the environmental map may be performed by other devices, not just the management device 600. For example, the control device of the work vehicle 100 may perform the global route design or the generation or editing of the environmental map.

[0035] The terminal device 400 is a computer used by a user located remotely from the work vehicle 100. While the terminal device 400 shown in FIG. 1 is a laptop computer, the present invention is not limited to this. The terminal device 400 may be a stationary computer such as a desktop personal computer (PC), or a mobile terminal such as a smartphone or tablet computer. The terminal device 400 may be used to remotely monitor or operate the work vehicle 100. For example, the terminal device 400 can display on a display image captured by one or more cameras (imaging devices) equipped on the work vehicle 100. The user can view the image to check the situation around the work vehicle 100 and send instructions to the work vehicle 100 to stop or start. The terminal device 400 can also display on a display a setting screen that allows the user to input information necessary to create a work plan for the work vehicle 100 (e.g., a schedule for each agricultural task). When the user inputs the necessary information on the setting screen and performs a send operation, the terminal device 400 transmits the input information to the management device 600. Management device 600 creates a work plan based on that information. Terminal device 400 can also be used to register one or more fields where work vehicle 100 will perform agricultural work, a storage location for work vehicle 100, and one or more waiting locations where work vehicle 100 will temporarily wait. Terminal device 400 may further have a function to display on the display a setting screen that allows the user to input information necessary to set a target route.

[0036] The configuration and operation of the system in this embodiment will be described in more detail below.

[0037] 2 is a side view that schematically shows an example of a work vehicle 100 and a work implement 300 coupled to the work vehicle 100. The work vehicle 100 in this embodiment can operate in both a manual driving mode and an automatic driving mode. In the automatic driving mode, the work vehicle 100 can travel unmanned. The work vehicle 100 can be driven automatically both inside and outside a field.

[0038] As shown in Fig. 2, work vehicle 100 includes a vehicle body 101, a prime mover (engine) 102, and a transmission 103. Vehicle body 101 is provided with wheels 104 with tires and a cabin 105. Wheels 104 include a pair of front wheels 104F and a pair of rear wheels 104R. Inside cabin 105, a driver's seat 107, a steering device 106, an operation terminal 200, and a group of switches for operation are provided. When work vehicle 100 travels in a field, one or both of front wheels 104F and rear wheels 104R may be replaced with a plurality of wheels (crawlers) equipped with tracks rather than with tires.

[0039] The work vehicle 100 is equipped with at least one sensing device that senses the environment around the work vehicle 100. In the example shown in Figure 2, the work vehicle 100 is equipped with multiple sensing devices. The sensing devices include multiple cameras 120, a LiDAR sensor 140, and multiple obstacle sensors 130.

[0040] Cameras 120 may be installed, for example, on the front, rear, left and right sides of work vehicle 100. Cameras 120 capture images of the environment around work vehicle 100 and generate image data. Images acquired by cameras 120 may be transmitted to terminal device 400 for remote monitoring. These images may be used to monitor work vehicle 100 during unmanned operation. Cameras 120 may also be used to generate images for recognizing surrounding features or obstacles, white lines, signs, or indications when work vehicle 100 travels on roads outside of fields (farm roads or public roads).

[0041] In the example of FIG. 2 , the LiDAR sensor 140 is disposed at the lower front portion of the vehicle body 101. The LiDAR sensor 140 may be disposed at another location. For example, the LiDAR sensor 140 may be disposed at the top of the cabin 105. The LiDAR sensor 140 may be a 3D-LiDAR sensor, but may also be a 2D-LiDAR sensor. The LiDAR sensor 140 senses the environment surrounding the work vehicle 100 and outputs sensing data. While the work vehicle 100 is traveling, the LiDAR sensor 140 repeatedly outputs sensor data indicating the distance and direction to each measurement point of an object present in the surrounding environment, or the two-dimensional or three-dimensional coordinate values ​​of each measurement point. The sensor data output from the LiDAR sensor 140 is processed by a control device of the work vehicle 100. The control device can estimate the self-position of the work vehicle 100 by matching the sensor data with an environmental map. The control device can further detect objects such as obstacles present around the work vehicle 100 based on the sensor data. The control device can also generate or edit an environmental map using an algorithm such as SLAM (Simultaneous Localization and Mapping). The work vehicle 100 may be equipped with multiple LiDAR sensors arranged in different positions and with different orientations.

[0042] The multiple obstacle sensors 130 shown in FIG. 2 are provided at the front and rear of the cabin 105. The obstacle sensors 130 may also be located in other locations. For example, one or more obstacle sensors 130 may be provided at any position on the side, front, and rear of the vehicle body 101. The obstacle sensors 130 may include, for example, a laser scanner or ultrasonic sonar. The obstacle sensors 130 are used to detect surrounding obstacles during autonomous driving and to stop or detour the work vehicle 100. A LiDAR sensor 140 may be used as one of the obstacle sensors 130.

[0043] The work vehicle 100 further includes a GNSS unit 110. The GNSS unit 110 includes a GNSS receiver. The GNSS receiver may include an antenna that receives signals from GNSS satellites and a processor that calculates the position of the work vehicle 100 based on the signals received by the antenna. The GNSS unit 110 receives satellite signals transmitted from multiple GNSS satellites and performs positioning based on the satellite signals. GNSS is a general term for satellite positioning systems such as GPS (Global Positioning System), QZSS (Quasi-Zenith Satellite System, e.g., Michibiki), GLONASS, Galileo, and BeiDou. In this embodiment, the GNSS unit 110 is provided on top of the cabin 105, but may be provided in another location.

[0044] The GNSS unit 110 may include an inertial measurement unit (IMU). Signals from the IMU can be used to supplement position data. The IMU can measure the tilt and minute movements of the work vehicle 100. By using data acquired by the IMU to supplement position data based on satellite signals, positioning performance can be improved.

[0045] The control device of the work vehicle 100 may use, for positioning, sensing data acquired by sensing devices such as the camera 120 or LiDAR sensor 140, in addition to the positioning results from the GNSS unit 110. If there are features that function as characteristic points in the environment in which the work vehicle 100 travels, such as farm roads, forest roads, public roads, or orchards, the position and orientation of the work vehicle 100 can be estimated with high accuracy based on the data acquired by the camera 120 or LiDAR sensor 140 and an environmental map that has been stored in advance in a storage device. The position of the work vehicle 100 can be identified with higher accuracy by correcting or complementing the position data based on satellite signals using the data acquired by the camera 120 or LiDAR sensor 140.

[0046] The prime mover 102 may be, for example, a diesel engine. An electric motor may be used instead of a diesel engine. The transmission 103 can change the propulsive force and travel speed of the work vehicle 100 by changing gears. The transmission 103 can also switch the work vehicle 100 between forward and reverse travel.

[0047] The steering device 106 includes a steering wheel, a steering shaft connected to the steering wheel, and a power steering device that assists steering by the steering wheel. The front wheels 104F are steerable wheels, and the traveling direction of the work vehicle 100 can be changed by changing the turning angle (also referred to as the "steering angle"). The steering angle of the front wheels 104F can be changed by operating the steering wheel. The power steering device includes a hydraulic device or an electric motor that supplies an assisting force for changing the steering angle of the front wheels 104F. When automatic steering is performed, the steering angle is automatically adjusted by the force of the hydraulic device or electric motor under control of a control device arranged in the work vehicle 100.

[0048] A coupling device 108 is provided at the rear of the vehicle body 101. The coupling device 108 includes, for example, a three-point support device (also referred to as a "three-point link" or "three-point hitch"), a PTO (Power Take Off) axle, a universal joint, and a communication cable. The coupling device 108 allows the work implement 300 to be attached to and detached from the work vehicle 100. The coupling device 108 can raise and lower the three-point link using, for example, a hydraulic device, thereby changing the position or attitude of the work implement 300. Power can also be transmitted from the work vehicle 100 to the work implement 300 via the universal joint. The work vehicle 100 can cause the work implement 300 to perform a predetermined task while towing the work implement 300. The coupling device may be provided at the front of the vehicle body 101. In this case, an implement can be connected to the front of the work vehicle 100.

[0049] 2 is a rotary tiller, but the work machine 300 is not limited to a rotary tiller. For example, any implement such as a seeder (seed sowing machine), a spreader (fertilizer applicator), a transplanter, a mower (grass cutting machine), a rake, a baler (grass collector), a harvester (harvesting machine), a sprayer, or a harrow can be connected to the work vehicle 100 and used.

[0050] 2 is capable of being driven by a driver, but may also be capable of being driven only unmanned. In that case, components required only for driven operation, such as the cabin 105, steering device 106, and driver's seat 107, may not be provided in the work vehicle 100. The unmanned work vehicle 100 can travel autonomously or by remote control by a user.

[0051] 3 is a block diagram showing an example configuration of the work vehicle 100 and the work implement 300. The work vehicle 100 and the work implement 300 can communicate with each other via a communication cable included in the coupling device 108. The work vehicle 100 can communicate with the terminal device 400 and the management device 600 via the network 80.

[0052] In the example of FIG. 3 , the work vehicle 100 includes a GNSS unit 110, a camera 120, an obstacle sensor 130, a LiDAR sensor 140, and an operation terminal 200, as well as a group of sensors 150 that detect the operating state of the work vehicle 100, a control system 160, a communication device 190, a group of operation switches 210, a buzzer 220, and a drive unit 240. These components are communicatively connected to each other via a bus. The GNSS unit 110 includes a GNSS receiver 111, an RTK receiver 112, an inertial measurement unit (IMU) 115, and a processing circuit 116. The group of sensors 150 includes a steering wheel sensor 152, a turning angle sensor 154, and an axle sensor 156. The control system 160 includes a memory device 170 and a control device 180. The control device 180 includes multiple electronic control units (ECUs) 181 to 186. The work machine 300 includes a drive unit 340, a control unit 380, and a communication unit 390. Note that Fig. 3 shows components that are relatively highly related to the operation of the autonomous driving by the work vehicle 100, and does not show other components.

[0053] The GNSS receiver 111 in the GNSS unit 110 receives satellite signals transmitted from multiple GNSS satellites and generates GNSS data based on the satellite signals. The GNSS data is generated in a predetermined format, such as the NMEA-0183 format. The GNSS data may include, for example, the identification number of each satellite from which the satellite signal is received, an elevation angle, an azimuth angle, and a value indicating reception strength. The reception strength may be expressed, for example, as a value such as the carrier-to-noise power density ratio (C / N0). The GNSS data may also include position information of the work vehicle 100 calculated based on the multiple received satellite signals, and information indicating the reliability of the position information. The position information may be represented, for example, by latitude, longitude, and height above mean sea level. The reliability of the position information may be represented, for example, by a DOP value indicating the satellite configuration.

[0054] The GNSS unit 110 shown in FIG. 3 performs positioning of the work vehicle 100 using RTK (Real Time Kinematic)-GNSS. FIG. 4 is a conceptual diagram showing an example of a work vehicle 100 performing positioning using RTK-GNSS. Positioning using RTK-GNSS uses correction signals transmitted from a reference station 60 in addition to satellite signals transmitted from multiple GNSS satellites 50. The reference station 60 can be installed near the field where the work vehicle 100 will be traveling (for example, within 10 km of the work vehicle 100). The reference station 60 generates correction signals, for example in RTCM format, based on the satellite signals received from the multiple GNSS satellites 50 and transmits them to the GNSS unit 110. The RTK receiver 112 includes an antenna and a modem, and receives the correction signals transmitted from the reference station 60. The processing circuit 116 of the GNSS unit 110 corrects the positioning results obtained by the GNSS receiver 111 based on the correction signal. By using RTK-GNSS, it is possible to perform positioning with an accuracy of, for example, a few centimeters. Position information including latitude, longitude, and altitude information is obtained through highly accurate positioning using RTK-GNSS. The GNSS unit 110 calculates the position of the work vehicle 100, for example, at a frequency of approximately 1 to 10 times per second.

[0055] The positioning method is not limited to RTK-GNSS, and any positioning method (such as interferometric positioning or relative positioning) that can obtain position information with the required accuracy can be used. For example, positioning may be performed using a virtual reference station (VRS) or a differential global positioning system (DGPS). If position information with the required accuracy can be obtained without using a correction signal transmitted from the reference station 60, the position information may be generated without using a correction signal. In this case, the GNSS unit 110 does not need to be equipped with an RTK receiver 112.

[0056] Even when RTK-GNSS is used, in locations where correction signals from the reference station 60 cannot be obtained (for example, on a road far from a field), the position of the work vehicle 100 is estimated by other methods without relying on signals from the RTK receiver 112. For example, the position of the work vehicle 100 can be estimated by matching data output from the LiDAR sensor 140 and / or camera 120 with a highly accurate environmental map.

[0057] The GNSS unit 110 in this embodiment further includes an IMU 115. The IMU 115 may include a three-axis acceleration sensor and a three-axis gyroscope. The IMU 115 may also include a direction sensor such as a three-axis geomagnetic sensor. The IMU 115 functions as a motion sensor and can output signals indicating various quantities such as the acceleration, velocity, displacement, and attitude of the work vehicle 100. The processing circuit 116 can estimate the position and orientation of the work vehicle 100 with higher accuracy based on the signals output from the IMU 115 in addition to the satellite signals and correction signals. The signals output from the IMU 115 can be used to correct or complement the position calculated based on the satellite signals and correction signals. The IMU 115 outputs signals at a higher frequency than the GNSS receiver 111. Using these high-frequency signals, the processing circuit 116 can measure the position and orientation of the work vehicle 100 at a higher frequency (e.g., 10 Hz or higher). A three-axis acceleration sensor and a three-axis gyroscope may be separately provided instead of the IMU 115. The IMU 115 may be provided as a device separate from the GNSS unit 110.

[0058] The camera 120 is an imaging device that captures images of the environment surrounding the work vehicle 100. The camera 120 includes an image sensor, such as a charge-coupled device (CCD) or a complementary metal oxide semiconductor (CMOS). The camera 120 may also include an optical system including one or more lenses and a signal processing circuit. The camera 120 captures images of the environment surrounding the work vehicle 100 while the work vehicle 100 is traveling and generates image (e.g., video) data. The camera 120 can capture video at a frame rate of, for example, 3 frames per second (fps) or higher. The images generated by the camera 120 can be used, for example, when a remote monitor uses the terminal device 400 to check the environment surrounding the work vehicle 100. The images generated by the camera 120 may be used for positioning or obstacle detection. As shown in FIG. 2 , multiple cameras 120 may be installed at different positions on the work vehicle 100, or a single camera may be installed. A visible light camera that generates a visible light image and an infrared camera that generates an infrared image may be provided separately. Both a visible light camera and an infrared camera may be provided as cameras that generate images for surveillance. The infrared camera can also be used to detect obstacles at night.

[0059] The obstacle sensor 130 detects objects present around the work vehicle 100. The obstacle sensor 130 may include, for example, a laser scanner or an ultrasonic sonar. The obstacle sensor 130 outputs a signal indicating the presence of an obstacle when an object is present closer than a predetermined distance from the obstacle sensor 130. Multiple obstacle sensors 130 may be provided at different positions on the work vehicle 100. For example, multiple laser scanners and multiple ultrasonic sonars may be arranged at different positions on the work vehicle 100. By providing such a large number of obstacle sensors 130, blind spots in monitoring obstacles around the work vehicle 100 can be reduced.

[0060] The steering wheel sensor 152 measures the rotation angle of the steering wheel of the work vehicle 100. The turning angle sensor 154 measures the turning angle of the front wheels 104F, which are the steered wheels. The measurement values ​​from the steering wheel sensor 152 and the turning angle sensor 154 are used for steering control by the control device 180.

[0061] The axle sensor 156 measures the rotational speed of the axle connected to the wheel 104, i.e., the number of rotations per unit time. The axle sensor 156 may be a sensor that uses, for example, a magnetoresistive element (MR), a Hall element, or an electromagnetic pickup. The axle sensor 156 outputs a numerical value that indicates, for example, the number of rotations per minute (unit: rpm) of the axle. The axle sensor 156 is used to measure the speed of the work vehicle 100.

[0062] The drive device 240 includes various devices necessary for the travel of the work vehicle 100 and the drive of the work implement 300, such as the prime mover 102, transmission 103, steering device 106, and coupling device 108 described above. The prime mover 102 may be equipped with an internal combustion engine such as a diesel engine. The drive device 240 may be equipped with an electric motor for traction instead of or in addition to the internal combustion engine.

[0063] The buzzer 220 is an audio output device that emits a warning sound to notify of an abnormality. For example, the buzzer 220 emits the warning sound when an obstacle is detected during autonomous driving. The buzzer 220 is controlled by the control device 180.

[0064] The storage device 170 includes one or more storage media, such as a flash memory or a magnetic disk. The storage device 170 stores various data generated by the GNSS unit 110, the camera 120, the obstacle sensor 130, the LiDAR sensor 140, the sensor group 150, and the control device 180. The data stored in the storage device 170 may include map data (environmental map) of the environment in which the work vehicle 100 travels and data on a global route (target route) for autonomous driving. The environmental map includes information on multiple fields in which the work vehicle 100 will perform agricultural work and the roads in their surrounding areas. The environmental map and target route may be generated by a processing device (processor) in the management device 600. The control device 180 may also have a function for generating or editing the environmental map and target route. The control device 180 can edit the environmental map and target route obtained from the management device 600 according to the travel environment of the work vehicle 100. The storage device 170 also stores data of the work plan received by the communication device 190 from the management device 600 .

[0065] The work plan includes information regarding a plurality of agricultural works to be performed by the work vehicle 100 over a plurality of work days. The work plan may be, for example, work schedule data including information on the scheduled times for each agricultural work to be performed by the work vehicle 100 on each work day.

[0066] The storage device 170 also stores computer programs that cause each ECU in the control device 180 to execute various operations, which will be described later. Such computer programs may be provided to the work vehicle 100 via a storage medium (e.g., a semiconductor memory or an optical disk) or an electric communication line (e.g., the Internet). Such computer programs may also be sold as commercial software.

[0067] The control device 180 includes a plurality of ECUs, such as an ECU 181 for speed control, an ECU 182 for steering control, an ECU 183 for implement control, an ECU 184 for automatic driving control, an ECU 185 for route generation, and an ECU 186 for map creation.

[0068] The ECU 181 controls the speed of the work vehicle 100 by controlling the prime mover 102 , the transmission 103 , and the brakes included in the drive device 240 .

[0069] The ECU 182 controls the steering of the work vehicle 100 by controlling the hydraulic device or electric motor included in the steering device 106 based on the measurement value of the steering wheel sensor 152 .

[0070] The ECU 183 controls the operation of the three-point link and the PTO shaft included in the coupling device 108, etc., in order to cause the work machine 300 to perform a desired operation. The ECU 183 also generates signals that control the operation of the work machine 300, and transmits these signals from the communication device 190 to the work machine 300.

[0071] The ECU 184 performs calculations and controls to achieve autonomous driving based on data output from the GNSS unit 110, the camera 120, the obstacle sensor 130, the LiDAR sensor 140, and the sensor group 150. For example, the ECU 184 identifies the position of the work vehicle 100 based on data output from at least one of the GNSS unit 110, the camera 120, and the LiDAR sensor 140. Within a farm field, the ECU 184 may determine the position of the work vehicle 100 based solely on data output from the GNSS unit 110. The ECU 184 may estimate or correct the position of the work vehicle 100 based on data acquired by the camera 120 or the LiDAR sensor 140. By utilizing the data acquired by the camera 120 or the LiDAR sensor 140, the accuracy of positioning can be further improved. Furthermore, outside the field, the ECU 184 estimates the position of the work vehicle 100 using data output from the LiDAR sensor 140 or the camera 120. For example, the ECU 184 may estimate the position of the work vehicle 100 by matching the data output from the LiDAR sensor 140 or the camera 120 with an environmental map. During autonomous driving, the ECU 184 performs calculations necessary for the work vehicle 100 to travel along a target route or a local route based on the estimated position of the work vehicle 100. The ECU 184 sends a speed change command to the ECU 181 and a steering angle change command to the ECU 182. In response to the speed change command, the ECU 181 changes the speed of the work vehicle 100 by controlling the prime mover 102, the transmission 103, or the brakes. In response to the steering angle change command, the ECU 182 changes the steering angle by controlling the steering device 106.

[0072] ECU 185 sequentially generates local paths that can avoid obstacles while work vehicle 100 is traveling along the target path. While work vehicle 100 is traveling, ECU 185 recognizes obstacles that exist around work vehicle 100 based on data output from camera 120, obstacle sensor 130, and LiDAR sensor 140. ECU 185 generates local paths that avoid the recognized obstacles.

[0073] ECU 185 may have a function for performing global route design instead of management device 600. In this case, ECU 185 determines the destination of work vehicle 100 based on the work plan stored in storage device 170, and determines a target route from the start point of work vehicle 100's movement to the destination point. ECU 185 can create, for example, a route that will reach the destination in the shortest time as the target route based on an environmental map containing road information stored in storage device 170. Alternatively, ECU 185 may generate, as the target route, a route that prioritizes specific types of roads (e.g., farm roads, roads that follow specific features such as waterways, or roads that can receive satellite signals from GNSS satellites well) based on attribute information of each road included in the environmental map.

[0074] The ECU 186 generates or edits a map of the environment in which the work vehicle 100 travels. In this embodiment, an environmental map generated by an external device such as the management device 600 is transmitted to the work vehicle 100 and recorded in the storage device 170. However, the ECU 186 can also generate or edit the environmental map instead. Below, an operation when the ECU 186 generates an environmental map will be described. The environmental map can be generated based on sensor data output from the LiDAR sensor 140. When generating the environmental map, the ECU 186 sequentially generates three-dimensional point cloud data based on the sensor data output from the LiDAR sensor 140 while the work vehicle 100 is traveling. The ECU 186 can generate the environmental map by connecting the sequentially generated point cloud data using an algorithm such as SLAM. The environmental map generated in this manner is a highly accurate three-dimensional map and can be used for self-localization estimation by the ECU 184. A two-dimensional map used for global path planning can be generated based on this three-dimensional map. In this specification, both the 3D map used for self-localization and the 2D map used for global route planning are referred to as “environment maps.” ECU 186 can also edit the map by adding various attribute information to the map, such as features (e.g., waterways, rivers, grass, trees, etc.) recognized based on the data output from camera 120 or LiDAR sensor 140, road type (e.g., whether it is a farm road), road surface condition, or road passability.

[0075] Through the actions of these ECUs, control device 180 realizes autonomous driving. During autonomous driving, control device 180 controls drive device 240 based on the measured or estimated position of work vehicle 100 and the target route. In this way, control device 180 can cause work vehicle 100 to travel along the target route.

[0076] The multiple ECUs included in the control device 180 can communicate with each other in accordance with a vehicle bus standard such as CAN (Controller Area Network). Instead of CAN, a faster communication method such as in-vehicle Ethernet (registered trademark) may be used. In FIG. 3 , each of the ECUs 181 to 186 is shown as an individual block, but the functions of each of these may be realized by multiple ECUs. An in-vehicle computer that integrates at least some of the functions of the ECUs 181 to 186 may also be provided. The control device 180 may include ECUs other than the ECUs 181 to 186, and any number of ECUs may be provided depending on the functions. Each ECU includes a processing circuit including one or more processors.

[0077] The communication device 190 includes circuits for communicating with the work machine 300, the terminal device 400, and the management device 600. The communication device 190 includes circuits for transmitting and receiving signals compliant with ISOBUS standards, such as ISOBUS-TIM, between the communication device 390 of the work machine 300. This allows the work machine 300 to perform desired operations and acquire information from the work machine 300. The communication device 190 may further include an antenna and communication circuits for transmitting and receiving signals via the network 80 between the communication devices of the terminal device 400 and the management device 600. The network 80 may include, for example, a cellular mobile communication network such as 3G, 4G, or 5G, and the Internet. The communication device 190 may also have a function for communicating with a mobile device used by a supervisor near the work vehicle 100. Communication between such mobile terminals can be performed in accordance with any wireless communication standard, such as Wi-Fi (registered trademark), cellular mobile communication such as 3G, 4G or 5G, or Bluetooth (registered trademark).

[0078] The operation terminal 200 is a terminal through which a user performs operations related to the travel of the work vehicle 100 and the operation of the work implement 300, and is also referred to as a virtual terminal (VT). The operation terminal 200 may include a display device such as a touch screen and / or one or more buttons. The display device may be, for example, a liquid crystal display or an organic light-emitting diode (OLED) display. By operating the operation terminal 200, a user can perform various operations, such as switching the autonomous driving mode on / off, recording or editing an environmental map, setting a target route, and switching the work implement 300 on / off. At least some of these operations can also be achieved by operating the operation switch group 210. The operation terminal 200 may be configured to be detachable from the work vehicle 100. A user located remote from the work vehicle 100 may operate the detached operation terminal 200 to control the operation of the work vehicle 100. The user may control the operation of the work vehicle 100 by operating a computer, such as a terminal device 400, on which necessary application software is installed, instead of the operation terminal 200.

[0079] 5 is a diagram showing an example of an operation terminal 200 and an operation switch group 210 provided inside the cabin 105. An operation switch group 210 including a plurality of switches that can be operated by the user is arranged inside the cabin 105. The operation switch group 210 may include, for example, a switch for selecting a gear position of the main transmission or the auxiliary transmission, a switch for switching between automatic driving mode and manual driving mode, a switch for switching between forward and reverse, and a switch for raising and lowering the work implement 300. Note that if the work vehicle 100 only performs unmanned operation and does not have a function for manned operation, the work vehicle 100 does not need to be equipped with the operation switch group 210.

[0080] The drive unit 340 in the work machine 300 shown in Figure 3 performs the operations required for the work machine 300 to perform a predetermined task. The drive unit 340 includes devices appropriate for the intended use of the work machine 300, such as a hydraulic system, an electric motor, or a pump. The control device 380 controls the operation of the drive unit 340. The control device 380 causes the drive unit 340 to perform various operations in response to signals transmitted from the work vehicle 100 via the communication device 390. In addition, a signal appropriate for the state of the work machine 300 can also be transmitted from the communication device 390 to the work vehicle 100.

[0081] Next, the configurations of the management device 600 and the terminal device 400 will be described with reference to Fig. 6. Fig. 6 is a block diagram illustrating a schematic hardware configuration of the management device 600 and the terminal device 400.

[0082] The management device 600 includes a storage device 650, a processor 660, a read-only memory (ROM) 670, a random access memory (RAM) 680, and a communication device 690. These components are communicatively connected to each other via a bus. The management device 600 manages the schedule of agricultural work performed in the field by the work vehicle 100 and can function as a cloud server that supports agriculture by utilizing the managed data. A user can input information necessary for creating a work plan using the terminal device 400 and upload that information to the management device 600 via the network 80. The management device 600 can create a schedule of agricultural work, i.e., a work plan, based on that information. The management device 600 can also generate or edit an environmental map. The environmental map may be distributed from a computer external to the management device 600.

[0083] The communication device 690 is a communication module for communicating with the work vehicle 100 and the terminal device 400 via the network 80. The communication device 690 can perform wired communication in accordance with communication standards such as IEEE 1394 (registered trademark) or Ethernet (registered trademark). The communication device 690 may also perform wireless communication in accordance with the Bluetooth (registered trademark) standard or the Wi-Fi standard, or cellular mobile communication such as 3G, 4G, or 5G.

[0084] The processor 660 may be, for example, a semiconductor integrated circuit including a central processing unit (CPU). The processor 660 may be implemented by a microprocessor or a microcontroller. Alternatively, the processor 660 may be implemented by a field programmable gate array (FPGA) equipped with a CPU, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), an application specific standard product (ASSP), or a combination of two or more circuits selected from these circuits. The processor 660 sequentially executes a computer program stored in the ROM 670, which describes a group of instructions for executing at least one process, to achieve the desired process.

[0085] The ROM 670 is, for example, a writable memory (e.g., a PROM), a rewritable memory (e.g., a flash memory), or a read-only memory. The ROM 670 stores a program that controls the operation of the processor 660. The ROM 670 does not have to be a single storage medium, but may be a collection of multiple storage media. Part of the collection of multiple storage media may be removable memory.

[0086] The RAM 680 provides a working area for temporarily loading the control program stored in the ROM 670 at boot time. The RAM 680 does not have to be a single storage medium, but may be a collection of multiple storage media.

[0087] The storage device 650 mainly functions as database storage. The storage device 650 may be, for example, a magnetic storage device or a semiconductor storage device. An example of a magnetic storage device is a hard disk drive (HDD). An example of a semiconductor storage device is a solid state drive (SSD). The storage device 650 may be a device independent of the management device 600. For example, the storage device 650 may be a storage device connected to the management device 600 via the network 80, such as cloud storage.

[0088] The terminal device 400 includes an input device 420, a display device 430, a storage device 450, a processor 460, a ROM 470, a RAM 480, and a communication device 490. These components are communicatively connected to one another via a bus. The input device 420 is a device for converting user instructions into data and inputting the data to a computer. The input device 420 may be, for example, a keyboard, a mouse, or a touch panel. The display device 430 may be, for example, a liquid crystal display or an organic EL display. The processor 460, ROM 470, RAM 480, storage device 450, and communication device 490 are described in the hardware configuration example of the management device 600, and therefore their description will be omitted.

[0089] 2. Operation Next, the operations of work vehicle 100, terminal device 400, and management device 600 will be described.

[0090] [2-1. Autonomous Driving Operation] First, an example of the operation of the automatic driving by the work vehicle 100 will be described. The work vehicle 100 in this embodiment can travel automatically both inside and outside the field. In the field, the work vehicle 100 drives the work implement 300 to perform predetermined agricultural work while traveling along a predetermined target route. If the obstacle sensor 130 detects an obstacle while traveling in the field, the work vehicle 100 may, for example, stop traveling, emit a warning sound from the buzzer 220, and send a warning signal to the terminal device 400. In the field, the positioning of the work vehicle 100 is performed mainly based on data output from the GNSS unit 110. On the other hand, outside the field, the work vehicle 100 travels automatically along a target route set on a farm road or public road outside the field. When working vehicle 100 is traveling outside the field, it travels using data acquired by camera 120 or LiDAR sensor 140. When working vehicle 100 detects an obstacle outside the field, it will, for example, avoid the obstacle or stop on the spot. Outside the field, the position of work vehicle 100 can be estimated based on the data output from LiDAR sensor 140 or camera 120 in addition to the positioning data output from GNSS unit 110.

[0091] An example of the operation of the work vehicle 100 when it travels automatically within a farm field will now be described.

[0092] FIG. 7 is a diagram schematically illustrating an example of a work vehicle 100 that automatically travels through a field along a target route. In this example, the field includes a work area 72 where the work vehicle 100 performs work using the work implement 300, and a headland 74 located near the outer periphery of the field. The user can set in advance which areas of the field on the map correspond to the work area 72 or the headland 74. The target route in this example includes multiple parallel main routes P1 and multiple turning routes P2 that connect the multiple main routes P1. The main routes P1 are located within the work area 72, and the turning routes P2 are located within the headland 74. Although each main route P1 shown in FIG. 7 is a straight route, each main route P1 may also include curved portions. The main path P1 can be automatically generated, for example, by a user viewing a map of the field displayed on the operation terminal 200 or the terminal device 400 and specifying two points near the edge of the field (points A and B in FIG. 7 ). In this case, multiple main paths P1 are set parallel to a line segment connecting points A and B specified by the user, and a target path within the field is generated by connecting these main paths P1 with a turning path P2. The dashed line in FIG. 7 represents the working width of the work implement 300. The working width is set in advance and recorded in the storage device 170. The working width can be set and recorded by the user operating the operation terminal 200 or the terminal device 400. Alternatively, the working width can be automatically recognized and recorded when the work implement 300 is connected to the work vehicle 100. The spacing between the multiple main paths P1 can be set to match the working width. The target path can be created based on user operation before autonomous driving begins. The target route can be created so as to cover, for example, the entire work area 72 in the field. The work vehicle 100 automatically travels along the target route as shown in Figure 7, making repeated round trips from the start point of the work to the end point of the work. Note that the target route shown in Figure 7 is merely an example, and the target route can be determined in any way.

[0093] Next, an example of control by the control device 180 during automatic operation in a farm field will be described.

[0094] FIG. 8 is a flowchart showing an example of the operation of steering control during automatic driving executed by the control device 180. The control device 180 performs automatic steering by executing the operations of steps S121 to S125 shown in FIG. 8 while the work vehicle 100 is traveling. The speed is maintained at, for example, a preset speed. While the work vehicle 100 is traveling, the control device 180 acquires data indicating the position of the work vehicle 100 generated by the GNSS unit 110 (step S121). Next, the control device 180 calculates the deviation between the position of the work vehicle 100 and the target route (step S122). The deviation represents the distance between the position of the work vehicle 100 at that time and the target route. The control device 180 determines whether the calculated position deviation exceeds a preset threshold (step S123). If the deviation exceeds the threshold, the control device 180 changes the steering angle by changing the control parameters of the steering device included in the drive device 240 so as to reduce the deviation. If the deviation does not exceed the threshold value in step S123, the operation of step S124 is skipped. In the following step S125, the control device 180 determines whether or not a command to end the operation has been received. A command to end the operation may be issued, for example, when a user remotely instructs the work vehicle 100 to stop autonomous driving, or when the work vehicle 100 reaches its destination. If a command to end the operation has not been issued, the process returns to step S121, and the same operation is performed based on the newly measured position of the work vehicle 100. The control device 180 repeats the operations of steps S121 to S125 until a command to end the operation is issued. The above operations are performed by the ECUs 182, 184 in the control device 180.

[0095] 8, the control device 180 controls the drive device 240 based only on the deviation between the position of the work vehicle 100 identified by the GNSS unit 110 and the target route, but the control may also take into consideration the deviation in heading. For example, when the heading deviation, which is the angular difference between the orientation of the work vehicle 100 identified by the GNSS unit 110 and the direction of the target route, exceeds a preset threshold, the control device 180 may change the control parameters (e.g., steering angle) of the steering device of the drive device 240 in accordance with the deviation.

[0096] An example of steering control by the control device 180 will be described in more detail below with reference to FIGS. 9A to 9D.

[0097] FIG. 9A is a diagram showing an example of a work vehicle 100 traveling along a target route P. FIG. 9B is a diagram showing an example of a work vehicle 100 shifted to the right from the target route P. FIG. 9C is a diagram showing an example of a work vehicle 100 shifted to the left from the target route P. FIG. 9D is a diagram showing an example of a work vehicle 100 facing in an inclined direction with respect to the target route P. In these figures, the pose indicating the position and orientation of the work vehicle 100 measured by the GNSS unit 110 is expressed as r(x, y, θ). (x, y) are coordinates representing the position of the reference point of the work vehicle 100 in the XY coordinate system, which is a two-dimensional coordinate system fixed to the Earth. In the examples shown in FIGS. 9A to 9D , the reference point of the work vehicle 100 is located at the position where the GNSS antenna on the cabin is installed, but the position of the reference point is arbitrary. θ is an angle representing the measured orientation of the work vehicle 100. In the illustrated example, the target path P is parallel to the Y axis, but in general, the target path P is not necessarily parallel to the Y axis.

[0098] As shown in FIG. 9A, if the position and orientation of the work vehicle 100 do not deviate from the target route P, the control device 180 maintains the steering angle and speed of the work vehicle 100 without changing them.

[0099] 9B , when the position of work vehicle 100 has shifted to the right from target route P, control device 180 changes the steering angle so that the traveling direction of work vehicle 100 tilts to the left and approaches route P. At this time, the speed may also be changed in addition to the steering angle. The magnitude of the steering angle can be adjusted, for example, according to the magnitude of position deviation Δx.

[0100] 9C , when the position of work vehicle 100 has shifted to the left from target route P, control device 180 changes the steering angle so that the traveling direction of work vehicle 100 tilts to the right and approaches route P. In this case, too, the speed may be changed in addition to the steering angle. The amount of change in the steering angle may be adjusted, for example, according to the magnitude of position deviation Δx.

[0101] As shown in FIG. 9D , when the position of the work vehicle 100 is not significantly deviated from the target route P but the heading is different from the direction of the target route P, the control device 180 changes the steering angle to reduce the azimuth deviation Δθ. In this case, the speed may also be changed in addition to the steering angle. The magnitude of the steering angle may be adjusted, for example, according to the magnitudes of the position deviation Δx and the azimuth deviation Δθ. For example, the smaller the absolute value of the position deviation Δx, the greater the amount of change in the steering angle according to the azimuth deviation Δθ. When the absolute value of the position deviation Δx is large, the steering angle will be changed significantly to return to the route P, which inevitably increases the absolute value of the azimuth deviation Δθ. Conversely, when the absolute value of the position deviation Δx is small, it is necessary to bring the azimuth deviation Δθ closer to zero. For this reason, it is appropriate to relatively increase the weight of the azimuth deviation Δθ (i.e., the control gain) used to determine the steering angle.

[0102] Control techniques such as PID control or MPC control (model predictive control) can be applied to the steering control and speed control of work vehicle 100. By applying these control techniques, it is possible to smooth the control that brings work vehicle 100 closer to target path P.

[0103] If an obstacle is detected by one or more obstacle sensors 130 while the work vehicle 100 is traveling, the control device 180 may, for example, stop the work vehicle 100. At this time, the buzzer 220 may be caused to emit a warning sound, or a warning signal may be sent to the terminal device 400. If it is possible to avoid the obstacle, the control device 180 may generate a local route that can avoid the obstacle, and control the drive device 240 so that the work vehicle 100 travels along that route.

[0104] Work vehicle 100 in this embodiment is capable of autonomous driving not only within a field but also outside the field. Outside the field, control device 180 can detect objects (e.g., other vehicles or pedestrians) that are located relatively far from work vehicle 100 based on data output from camera 120 or LiDAR sensor 140. Control device 180 generates a local route to avoid the detected object, and performs speed control and steering control along the local route, thereby realizing autonomous driving on roads outside the field.

[0105] In this way, the work vehicle 100 in this embodiment can autonomously travel unmanned within and outside a field. FIG. 10 is a diagram schematically illustrating an example of a situation in which multiple work vehicles 100 are autonomously traveling inside a field 70 and on a road 76 outside the field 70. An environmental map and a target route of an area including multiple fields and their surrounding roads are stored in the storage device 170. The environmental map and the target route can be generated by the management device 600 or the ECU 185. When the work vehicle 100 travels on a road, the work vehicle 100 travels along the target route with the work implement 300 raised while sensing the surroundings using sensing devices such as the camera 120 and the LiDAR sensor 140. During travel, the control device 180 sequentially generates local routes and causes the work vehicle 100 to travel along the local routes. This enables autonomous travel while avoiding obstacles. The target route may be changed during travel depending on the situation.

[0106] [2-2. Local Path Planning] When the work vehicle 100 is traveling outside a field, an obstacle such as a pedestrian or another vehicle may be present on or near the global path. To prevent the work vehicle 100 from colliding with the obstacle, the ECU 185 in the control device 180 sequentially generates a local path that can avoid the obstacle while the work vehicle 100 is traveling. The ECU 185 generates the local path based on sensing data acquired by sensing devices (such as the obstacle sensor 130, the LiDAR sensor 140, and the camera 120) equipped on the work vehicle 100 while the work vehicle 100 is traveling. The local path is defined by a plurality of waypoints along a portion of the second path 30B. The ECU 185 determines whether an obstacle is present on or near the path of the work vehicle 100 based on the sensing data. If such an obstacle is present, the ECU 185 sets a plurality of waypoints to avoid the obstacle and generates a local path. If no obstacles are present, ECU 185 generates a local route that is approximately parallel to second route 30B. Information indicating the generated local route is sent to ECU 184 for automatic driving control. ECU 184 controls ECU 181 and ECU 182 so that work vehicle 100 travels along the local route. This allows work vehicle 100 to travel while avoiding obstacles. Note that if there are traffic lights on the road on which work vehicle 100 is traveling, work vehicle 100 may, for example, recognize the traffic lights based on images captured by camera 120, stop when the light is red, and start when the light is green.

[0107] FIG. 11 is a diagram showing an example of a global route and a local route generated in an environment where obstacles are present. In FIG. 11, a global route 30 is illustrated by a dotted arrow, and a local route 32 generated sequentially during travel is illustrated by a solid arrow. The global route 30 is defined by a plurality of waypoints 30p. The local route 32 is defined by a plurality of waypoints 32p set at intervals shorter than the intervals between the waypoints 30p. Each waypoint has, for example, position and orientation information. The management device 600 generates the global route 30 by setting the plurality of waypoints 30p at a plurality of locations, including intersections on a road 76. The intervals between the waypoints 30p may be relatively long, for example, several meters to several tens of meters. The ECU 185 generates the local route 32 by setting the plurality of waypoints 32p based on sensing data output from the sensing device while the work vehicle 100 is traveling. The intervals between waypoints 32p on the local route 32 are shorter than the intervals between waypoints 30p on the global route 30. The intervals between waypoints 32p can be, for example, several tens of centimeters (cm) to several meters (m). The local route 32 is generated within a relatively small range (for example, a range of about several meters) starting from the position of the work vehicle 100. FIG. 11 shows an example of a series of local routes 32 generated while the work vehicle 100 travels along a road 76 between fields 70 and turns left at an intersection. While the work vehicle 100 is moving, the ECU 185 repeats the operation of generating a local route from the position of the work vehicle 100 estimated by the ECU 184 to a point, for example, several meters ahead. The work vehicle 100 travels along the local routes that are sequentially generated.

[0108] In the example shown in FIG. 11 , an obstacle 40 (e.g., a person) is present ahead of the work vehicle 100. In FIG. 11 , an example of a range sensed by a sensing device such as a camera 120, an obstacle sensor 130, or a LiDAR sensor 140 mounted on the work vehicle 100 is illustrated as a sector. In such a situation, the ECU 185 generates a local route 32 so as to avoid the obstacle 40 detected based on the sensing data. The ECU 185 determines whether there is a possibility that the work vehicle 100 will collide with the obstacle 40 based on, for example, the sensing data and the width of the work vehicle 100 (including the width of an implement if one is attached). If there is a possibility that the work vehicle 100 will collide with the obstacle 40, the ECU 185 sets a plurality of waypoints 32p so as to avoid the obstacle 40, and generates the local route 32. The ECU 185 may recognize not only the presence or absence of an obstacle 40 but also the condition of the road surface (e.g., mud, depressions, etc.) based on sensing data, and if a location where travel is difficult is detected, it may generate a local route 32 to avoid such a location. The work vehicle 100 travels along the local route 32. If the obstacle 40 cannot be avoided no matter how the local route 32 is set, the control device 180 may stop the work vehicle 100. At this time, the control device 180 may send a warning signal to the terminal device 400 to alert the monitor. After stopping, if it is recognized that the obstacle 40 has moved and there is no longer a risk of collision, the control device 180 may resume travel of the work vehicle 100.

[0109] 12 is a flowchart showing the route planning and travel control operations in this embodiment. By executing the operations of steps S141 to S146 shown in FIG. 12, route planning can be performed and the automatic travel of work vehicle 100 can be controlled.

[0110] 12 , the management device 600 first acquires a map and a work plan from the storage device 650 (step S141). Next, the management device 600 performs global route design for the work vehicle 100 using the method described above based on the map and the work plan (step S142). The global route design can be performed at any timing before the work vehicle 100 starts traveling. The global route design may be performed immediately before the work vehicle 100 starts traveling, or may be performed one day or earlier before the work vehicle 100 starts traveling. The global route may also be generated based on information (e.g., starting point, destination point, waypoints, etc.) input by the user using the terminal device 400. As described above, when generating a route to a field or a route from the field to another location (e.g., a storage location or waiting location for the work vehicle 100), the management device 600 generates at least one of the following routes for the work vehicle 100: a route that prioritizes farm roads, a route that prioritizes roads that follow specific features, and a route that prioritizes roads on which satellite signals can be received normally, based on attribute information for each road on the map. The management device 600 transmits data indicating the generated global route to the work vehicle 100. The management device 600 then issues a travel instruction to the work vehicle 100 at a predetermined timing. In response to this, the control device 180 of the work vehicle 100 controls the drive unit 240 to start traveling the work vehicle 100 (step S143). This causes the work vehicle 100 to start traveling. The timing to start traveling can be set, for example, to an appropriate timing that enables the work vehicle 100 to arrive at the field by the scheduled start time of the first agricultural work on each work day indicated in the work plan. While the work vehicle 100 is traveling, the ECU 185 of the control device 180 uses the method described above to design a local route to avoid a collision with an obstacle (step S144). If no obstacle is detected, the ECU 185 generates a local route that is approximately parallel to the global route. If an obstacle is detected, the ECU 185 generates a local route that can avoid the obstacle. Next, the ECU 184 determines whether or not to end the traveling of the work vehicle 100 (step S145). For example, if a local route that can avoid the obstacle could not be generated, or if the work vehicle 100 has arrived at the destination, the ECU 184 stops the work vehicle 100 (step S146).If no obstacle is detected, or if a local route that can avoid the obstacle has been generated, the process returns to step S143, and ECU 184 causes work vehicle 100 to travel along the generated local route. Thereafter, the operations of steps S143 to S145 are repeated until it is determined in step S145 that travel has ended.

[0111] Through the above operations, the work vehicle 100 can automatically travel along the generated route without colliding with any obstacles.

[0112] In the example of FIG. 12 , once the global route is generated, it remains unchanged until the destination is reached. This example is not limiting; the global route may be modified while the work vehicle 100 is traveling. For example, while the work vehicle 100 is traveling, the ECU 185 may recognize at least one of the condition of the road on which the work vehicle 100 is traveling, the condition of the vegetation around the work vehicle 100, and the weather condition based on sensing data acquired by a sensing device such as the camera 120 or the LiDAR sensor 140, and may change the global route if the recognized condition satisfies a predetermined condition. When the work vehicle 100 is traveling along the global route, some roads may be impassable. For example, heavy rain may cause the road to become muddy, the road surface may collapse, or the road may become impassable due to an accident or other cause. Alternatively, the vegetation around a farm road may grow longer than expected, or new buildings may have been constructed, making it difficult to receive satellite signals from GNSS satellites. Taking such situations into consideration, ECU 185 may detect roads that are difficult to travel based on sensing data acquired while work vehicle 100 is traveling, and change the route to avoid such roads. Furthermore, when ECU 185 changes the route, it may store the changed route in storage device 170 and transmit information about the changed route to management device 600. In that case, management device 600 may use the changed route the next time it generates a route to the same field. This enables flexible route planning that responds to changes in the traveling environment.

[0113] [2-3. Cruise control system and cruise control method] The management device 600 according to this embodiment functions as a cruise control system for an agricultural machine (in this example, the work vehicle 100) that drives automatically while sensing the surrounding environment. The cruise control system according to this embodiment is primarily used while the work vehicle 100 is traveling automatically outside of a field, but may also be used when the work vehicle 100 is traveling automatically within a field.

[0114] The management device 600, which functions as a driving control system according to this embodiment, includes a storage device 650 that stores the driving history of the work vehicle 100, and a processing device (processor) 660. The driving history of the work vehicle 100 stored in the storage device 650 includes at least information on the past driving conditions of the work vehicle 100 and the positions of obstacle candidates detected during past driving. The processing device 660 causes the work vehicle 100 to perform an avoidance operation to avoid an obstacle, based on sensor data obtained by sensing the environment around the work vehicle 100 and the driving history of the work vehicle 100 stored in the storage device 650.

[0115] The driving control method according to this embodiment is a driving control method for agricultural machinery (in this example, work vehicle 100) that drives automatically while sensing the surrounding environment, and includes storing a driving history including information on the past driving conditions of the work vehicle 100 and the positions of potential obstacles detected during past driving, and causing the work vehicle 100 to perform avoidance operations to avoid obstacles based on sensor data obtained by sensing the environment around the work vehicle 100 and the driving history of the work vehicle 100.

[0116] An "obstacle" refers to an object that impedes the travel of an agricultural machine, and includes, for example, a moving or stationary object on the ground, and a part of the ground in a specific state. Examples of obstacles include moving objects on the ground (dynamic obstacles), such as people (passersby), animals (e.g., deer, raccoon dogs, wild boars, bears), and other vehicles, while stationary objects on the ground (static obstacles), such as stones, rocks, fallen trees, utility poles, guardrails, buildings, moats, and tree branches that extend into the road. Examples of "parts of the ground in a specific state" include depressions, irregularities (missing parts, protrusions) that impede travel, mud, cracks in the road, and the like. An "obstacle candidate" refers to an object that is detected as a possible obstacle, and may include objects that are not actually obstacles (i.e., objects that do not impede the travel of an agricultural machine). Examples of objects that are not actually obstacles but may be detected as obstacle candidates include weeds, small amounts of snow, small puddles, second-crop rice grains, and uneven ground (plow marks) that do not hinder travel. The "traveling state of the agricultural machine" is defined by parameters such as the agricultural machine's speed, acceleration, direction of travel, target route, and the positions and directions of each of a number of waypoints that define the target route. A "change in the traveling state of the agricultural machine" refers to changing at least one of the parameters that define the traveling state of the agricultural machine. An "avoidance operation" not only refers to changing the traveling state of the agricultural machine, but also includes increasing the alert level in sensing of the surrounding environment (specifically, increasing the amount of data acquired by sensing) without changing the traveling state of the agricultural machine.

[0117] The work vehicle 100 performs autonomous driving while sensing the surrounding environment with one or more sensing devices possessed by the work vehicle 100. For example, in the example of FIG. 2 , the sensing devices possessed by the work vehicle 100 include multiple cameras 120, a LiDAR sensor 140, and multiple obstacle sensors 130. Without being limited to this, the sensing devices of the work vehicle 100 may include at least one of the cameras 120, the obstacle sensors 130, and the LiDAR sensors 140. The work vehicle 100 performs autonomous driving while sensing the surrounding environment with at least one of the sensing devices possessed by the work vehicle 100. The processing device 660 acquires sensor data acquired by at least one of the sensing devices possessed by the work vehicle 100, such as image data acquired by the camera 120, data (e.g., point cloud data) output from the obstacle sensor 130 or the LiDAR sensor 140, etc.

[0118] 13A and 13B are diagrams schematically illustrating an example of an area that can be sensed by the LiDAR sensor 140 and the camera 120 of the work vehicle 100. In the example shown in FIGS. 13A and 13B, the work vehicle 100 has four LiDAR sensors 140 (140a, 140b, 140c, and 140d) and four cameras 120 (120a, 120b, 120c, and 120d) provided on the front, rear, left, and right sides of the work vehicle 100, and can sense the surrounding environment using these. In FIGS. 13A and 13B, an example of the area sensed by the LiDAR sensor 140 and the camera 120 is shown as a gray sector or triangle.

[0119] The LiDAR sensor 140 emits laser beam pulses (hereinafter referred to as "laser pulses") one after another while changing the emission direction, and can measure the distance to each reflection point from the time difference between the emission time and the time when the reflected light of each laser pulse is acquired (ToF (Time of Flight) method). Alternatively, the LiDAR sensor 140 may be a sensor that measures distance using FMCW (Frequency Modulated Continuous Wave) technology. A LiDAR sensor using FMCW technology emits laser light whose frequency is linearly modulated and can determine the distance and speed to the reflection point based on the frequency of a beat signal obtained by detecting the interference light between the emitted light and the reflected light. The "reflection point" may be a point on the surface of an object located in the environment surrounding the work vehicle 100.

[0120] The LiDAR sensor 140 can measure the distance from the LiDAR sensor 140 to an object using any method. Measurement methods for the LiDAR sensor 140 include, for example, mechanical rotation, MEMS, and phased array methods. These measurement methods differ in the way they emit laser pulses (scanning methods). For example, a mechanical rotation LiDAR sensor rotates a cylindrical head that emits laser pulses and detects the reflected light of the laser pulses to scan the surrounding environment in all directions 360 degrees around the rotation axis. A MEMS LiDAR sensor uses a MEMS mirror to oscillate the emission direction of the laser pulses and scan the surrounding environment within a predetermined angular range centered on the oscillation axis. A phased array LiDAR sensor controls the phase of light to oscillate the emission direction of light and scan the surrounding environment within a predetermined angular range centered on the oscillation axis.

[0121] An example of a procedure in which the processing device 660 causes the work vehicle 100 to execute an avoidance operation to avoid an obstacle (including changing the traveling state of the work vehicle 100 and / or changing the alert level in sensing the surrounding environment) will be described with reference to Figure 14. Figure 14 is a flowchart showing an example of a procedure in which the processing device 660 causes the work vehicle 100 to execute an avoidance operation to avoid an obstacle. The flowchart in Figure 14 shows a procedure used while the work vehicle 100 is traveling automatically while sensing the surrounding environment, and is mainly an example of a procedure used after an obstacle candidate has been detected.

[0122] In step S161, when an object is detected based on the sensor data, the processing device 660 determines whether the detected object is an obstacle candidate. Object detection may be performed continuously while the work vehicle 100 is performing autonomous driving while sensing the surrounding environment, or may be performed at predetermined time intervals. The determination of whether a detected object is an obstacle candidate is performed based on predetermined criteria. For example, if the detected object is an object on the ground, the processing device 660 may detect the width and height of the object and determine it as an obstacle candidate if the width and / or height of the object are equal to or greater than a predetermined value. If the detected object is a portion of the ground in a specific state, the processing device 660 may detect the width and length of that portion and determine it as an obstacle candidate if the width and / or length of the portion of the ground in the specific state are equal to or greater than a predetermined value. The width of the object or the portion of the ground in the specific state is the length of the object or the portion of the ground in the specific state in a direction substantially parallel to the width (vehicle body width) of the work vehicle 100. When determining whether or not an object is an obstacle candidate, the processing device 660 may compare the width of the object or the width of a portion of the ground in a specific state with the width of the work vehicle 100. If a work implement 300 is coupled to the work vehicle 100, the processing device 660 may compare the width of the object or the width of a portion of the ground in a specific state with the width of the work vehicle 100 and / or the width of the work implement 300 when determining whether or not an object is an obstacle candidate.

[0123] Alternatively, the processing device 660 may identify the type of the detected object and determine whether it is an obstacle candidate, for example, based on image data of the object acquired by the camera 120. For example, the processing device 660 may determine that the detected object is an obstacle candidate if it is a moving object (a person (pedestrian), an animal, another vehicle, etc.).

[0124] In step S161, if it is determined that the object detected based on the sensor data is an obstacle candidate (that is, if an obstacle candidate is detected based on the sensor data), the process proceeds to step S162.

[0125] In step S162, processing device 660 determines the avoidance operation to be performed by work vehicle 100 (a change in the driving state of work vehicle 100 and / or a change in the alert level in sensing of the surrounding environment) based on the sensor data and the driving history of work vehicle 100 stored in storage device 650. Step S162 also includes cases where processing device 660 determines, based on the sensor data and the driving history of work vehicle 100, that it is not necessary to have work vehicle 100 perform an avoidance operation (i.e., to maintain both the driving state of work vehicle 100 and the alert level in sensing of the surrounding environment unchanged).

[0126] In step S163, processing device 660 causes work vehicle 100 to perform the avoidance operation determined in step S162.

[0127] For example, if the driving history of the work vehicle 100 stored in the storage device 650 includes information on an obstacle candidate previously detected at the same location, the processing device 660 causes the work vehicle 100 to perform an avoidance operation using the driving state data of the work vehicle 100 included in the driving history of the work vehicle 100. The processing device 660 may, for example, vary the avoidance operation that the work vehicle 100 performs depending on the type of obstacle candidate. When the detected obstacle candidate is a stationary object, the processing device 660 changes the target route of the work vehicle 100 so as to avoid the detected obstacle candidate, for example, by the method described with reference to FIGS. 11 and 12. When the detected obstacle candidate is a moving object (a person, an animal, another vehicle, etc.), the processing device 660 stops the work vehicle 100 for a predetermined time, senses the surrounding environment after the predetermined time has elapsed, and if no obstacle candidate is detected, resumes the driving of the work vehicle 100. When the driving history of the work vehicle 100 is resumed, the speed of the work vehicle 100 may be set low (for example, the work vehicle 100 may be slowed down). After a predetermined time has elapsed, the surrounding environment is sensed, and if an obstacle candidate is detected, the target route of the work vehicle 100 is changed so as to avoid the obstacle candidate, for example, using the method described with reference to Figures 11 and 12.

[0128] In step S164, the processing device 660 adds the avoidance operation that the work vehicle 100 was caused to perform in step S163 and information about the position where the obstacle candidate was detected to the driving history of the work vehicle 100 stored in the storage device 650. The processing device 660 may acquire information about the position where the detected obstacle candidate is located when it is determined that the detected object is an obstacle candidate, or may acquire information about the position of the object when it is detected. The processing device 660 may add information about the position where the obstacle candidate was detected and the changed driving state of the work vehicle 100 to the driving history only when the driving state of the work vehicle 100 is changed in step S163. The processing device 660 may also add information about the position where the obstacle candidate was detected and the driving state of the work vehicle 100 to the driving history even when the driving state of the work vehicle 100 is not changed in step S163 and only the alert level in the sensing of the work vehicle 100 is changed. The processing device 660 may add information on the location where the obstacle candidate was detected and the driving state of the work vehicle 100 to the driving history even when neither the driving state of the work vehicle 100 nor the alert level in the sensing of the work vehicle 100 is changed in step S163.

[0129] The processing device 660 repeats steps S161 to S164 until a command to end the operation is issued (step S165).

[0130] As described with reference to FIG. 14 , when the processing device 660 detects an obstacle candidate during the autonomous driving of the work vehicle 100, it causes the work vehicle 100 to perform an avoidance operation to avoid the obstacle based on the sensor data and the driving history of the work vehicle 100. The processing device 660 can, for example, change or generate a target route for the work vehicle 100 so as to avoid the obstacle, thereby preventing the work vehicle 100 from getting stuck. Furthermore, when the processing device 660 detects an object that is not actually an obstacle (e.g., weeds, a small amount of snow, a small puddle, etc.) as an obstacle candidate, and the object does not hinder the driving of the work vehicle 100, it can cause the work vehicle 100 to drive without changing the target route. The processing device 660 can control the driving of the work vehicle 100 so that it is performed efficiently.

[0131] Furthermore, the processing device 660 stores the position of an obstacle avoided by the work vehicle 100 and the driving state of the work vehicle 100 at that time in the storage device 650 as a driving history, so that when a similar obstacle is detected in subsequent driving, the processing device 660 can refer to the driving history of the work vehicle 100 and use the driving state of the work vehicle 100 at that time. Since the driving of the work vehicle 100 can be controlled based on the past driving history (track record), the driving of the work vehicle 100 can be performed more stably and efficiently. The processing device 660 can further improve the efficiency of the automatic driving of the work vehicle 100 by controlling the driving of the work vehicle 100 by referring to the driving history of the work vehicle 100 in addition to the sensor data. The processing device 660 can improve the accuracy of the driving control of the work vehicle 100 by adding (feeding back) the driving state of the work vehicle 100 to the driving history each time the work vehicle 100 detects an obstacle candidate.

[0132] Furthermore, even if the processing device 660 determines that an object detected as an obstacle candidate is not an obstacle (for example, weeds, a small amount of snow, a small puddle, etc.), it can refer to the past driving history to reduce the possibility of overlooking an obstacle hidden by the detected object (for example, unevenness in the ground (sinkholes, gaps, protrusions, etc.) hidden by weeds or snow).

[0133] As described above, the driving history of the work vehicle 100 stored in the storage device 650 includes at least information on the past driving conditions of the work vehicle 100 and the positions of obstacle candidates detected during past driving. The driving history of the work vehicle 100 stored in the storage device 650 may further include at least one of the following information. For example, information on the type of obstacle candidate detected during past driving of the work vehicle 100, information on the size of the obstacle candidate, information on whether the obstacle candidate was determined to be an obstacle, information on the date and time the obstacle candidate was detected, information on the weather when the obstacle candidate was detected, and the type of work implement coupled to the work vehicle 100. For example, if the obstacle candidate is an object on the ground, the size of the obstacle candidate includes the width and height of the object, and if the obstacle candidate is a portion of the ground in a specific state, the size includes the width and length of the portion of the ground in the specific state. When a work implement 300 is coupled to the work vehicle 100, the processing device 660 can vary the avoidance operation to be performed by the work vehicle 100 depending on the width and / or type of the work implement 300.

[0134] Another example of the procedure by which the processing device 660 causes the work vehicle 100 to perform an avoidance operation to avoid an obstacle will be described with reference to FIG. 15 . FIG. 15 is a flowchart showing an example of the procedure by which the processing device 660 causes the work vehicle 100 to perform an avoidance operation to avoid an obstacle (here, changing the alert level in sensing the surrounding environment). The procedure of the flowchart in FIG. 15 is used while the work vehicle 100 is traveling automatically while sensing the surrounding environment, and can be used regardless of whether an obstacle candidate has been detected. The processing device 660 may execute the procedure of the flowchart in FIG. 15 and the procedure of the flowchart in FIG. 14 in parallel. For example, the processing device 660 may execute the procedure of the flowchart in FIG. 15 when it is determined in step S161 of the flowchart in FIG. 14 that an object detected based on sensor data is not an obstacle candidate (when proceeding to "No").

[0135] In step S181, while work vehicle 100 is traveling, processing device 660 determines the alert level for sensing the surrounding environment based on the travel history of work vehicle 100 stored in storage device 650. The alert level for sensing the surrounding environment may be set to, for example, a plurality of levels (stages), and the alert level may be determined by selecting one of these levels.

[0136] In step S182, the processing device 660 causes the work vehicle 100 to sense the surrounding environment at the alert level determined in step S181. Based on the alert level determined in step S181, the processing device 660 either increases, decreases, or maintains (does not change) the alert level for sensing the surrounding environment of the work vehicle 100.

[0137] For example, the processing device 660 increases the alert level for sensing the surrounding environment when the driving history of the work vehicle 100 includes information on obstacle candidates that were previously detected on the target route of the work vehicle 100, and maintains the alert level for sensing the surrounding environment when the driving history of the work vehicle 100 does not include information on obstacle candidates that were previously detected on the target route of the work vehicle 100. Alternatively, the processing device 660 may set the alert level for sensing the surrounding environment higher when the driving history of the work vehicle 100 includes information on obstacle candidates that were previously detected on the target route of the work vehicle 100 than when the driving history of the work vehicle 100 does not include information on obstacle candidates that were previously detected on the target route of the work vehicle 100.

[0138] When the processing device 660 increases the alert level in sensing the surrounding environment, it increases the frequency of sensor data acquisition or widens the range of the surrounding environment from which sensor data is acquired, thereby increasing the amount of acquired sensor data. When the processing device 660 decreases the alert level in sensing the surrounding environment, it decreases the frequency of sensor data acquisition or narrows the range of the surrounding environment from which sensor data is acquired, thereby decreasing the amount of acquired sensor data. For example, the amount of acquired sensor data is increased by changing the size of the range sensed by the LiDAR sensor 140 and the camera 120 shown in FIGS. 13A and 13B. The range sensed by the LiDAR sensor 140 can be changed, for example, by changing the maximum distance from the LiDAR sensor 140. FIG. 13A shows the maximum distance L from the LiDAR sensor 140a for the range Rsa sensed by the LiDAR sensor 140a. ａ This shows:

[0139] The size of the range sensed by the LiDAR sensor 140 can be changed, for example, by changing the data portion of the three-dimensional point cloud data output by the LiDAR sensor 140 that is used for object detection. The three-dimensional point cloud data output by the LiDAR sensor 140 includes multiple points. The points to be used for object detection are selected based on the distance between the LiDAR sensor 140 and the points. By changing the size of the distance that serves as the basis for this selection, the size of the range sensed by the LiDAR sensor 140 can be changed. When the distance from the LiDAR sensor 140 is L ａ By detecting only the following points, the maximum distance from the LiDAR sensor 140 is L ａcan be set as the range sensed by LiDAR sensor 140. Alternatively, the size of the range sensed by LiDAR sensor 140 may be changed by changing the range scanned by LiDAR sensor 140. For example, the size of the maximum distance from LiDAR sensor 140 in the search region may be changed by changing the output of the laser pulse emitted from LiDAR sensor 140.

[0140] The processing device 660 repeats steps S181 and S182 until a command to end the operation is issued (step S183).

[0141] As described with reference to FIG. 15 , even if an obstacle candidate is not actually detected by sensing performed during the autonomous driving of the work vehicle 100, the processing device 660 can increase the alert level for sensing the surrounding environment when approaching a location where an obstacle candidate was previously detected or its vicinity, thereby enabling efficient detection of obstacle candidates. Furthermore, even if an obstacle that was previously avoided is not detected for some reason, for example, if it is hidden by weeds or snow or if sensing accuracy is temporarily reduced, by utilizing past driving history (track record), it is possible to suppress overlooking of obstacles and suppress a decrease in the driving efficiency of the autonomously driven work vehicle 100. For example, based on the past driving history, it is possible to select areas where obstacle candidates are likely to exist, such as by focusing sensing on areas where obstacle candidates are likely to exist. The processing device 660 can control the autonomous driving of the work vehicle 100 so that the driving is efficient.

[0142] 15 , the avoidance action that the processing device 660 causes the work vehicle 100 to perform is simply to set an alert level in sensing the surrounding environment, but this is not limited to this example. For example, in step S181, while the work vehicle 100 is traveling, the processing device 660 may determine the alert level in sensing the surrounding environment and also determine the traveling state of the work vehicle 100 based on the traveling history of the work vehicle 100 stored in the storage device 650. For example, the alert level in sensing the surrounding environment may be increased and the speed of the work vehicle 100 may be reduced (for example, the work vehicle 100 may be caused to travel slowly).

[0143] A more detailed example of the procedure by which the processing device 660 causes the work vehicle 100 to perform an avoidance operation to avoid an obstacle will be described below.

[0144] Another example of the procedure by which the processing device 660 causes the work vehicle 100 to execute an avoidance operation to avoid an obstacle will be described with reference to Figure 16. Figure 16 is a flowchart showing another example of the procedure by which the processing device 660 causes the work vehicle 100 to execute an avoidance operation to avoid an obstacle. Detailed description of steps common to the flowchart in Figure 14 may be omitted.

[0145] In step S201, when an object is detected based on sensor data, the processing device 660 determines whether the detected object is an obstacle candidate. If it is determined in step S201 that the object detected based on the sensor data is an obstacle candidate, the process proceeds to step S202.

[0146] In step S202, the processing device 660 determines whether the detected obstacle candidate is an obstacle. If it is determined in step S202 that the detected obstacle candidate is an obstacle, the process proceeds to step S203.

[0147] In step S203, the processing device 660 determines whether a similar obstacle (e.g., an obstacle of the same type) has been detected in the past at the same position or a nearby position by referencing the travel history of the work vehicle 100 stored in the storage device 650. The processing device 660 refers to the travel history of the work vehicle 100 stored in the storage device 650 and checks whether the history contains information about an obstacle of the same type that has been detected in the past at the same or a nearby position. If it is determined in step S203 that an obstacle of the same type has been detected in the past at the same or a nearby position, the process proceeds to step S204. If it is determined in step S203 that an obstacle of the same type has not been detected in the past at the same or a nearby position, the process proceeds to step S205.

[0148] In step S204, the processing device 660 acquires data on the driving state of the work vehicle 100 when the same type of obstacle was detected at the same or a nearby location in the past from the storage device 650, and changes the driving state of the work vehicle 100 based on the acquired data.

[0149] In step S205, the processing device 660 changes the traveling state of the work vehicle 100 so as to avoid the obstacle. The processing device 660 may change the traveling state of the work vehicle 100 based on a predetermined method (default parameters) for changing the traveling state of the work vehicle 100. The processing device 660 may change the traveling state of the work vehicle 100 so as to avoid the obstacle using the method described with reference to Figures 11 and 12.

[0150] After step S204 or step S205, in step S206, the processing device 660 stores information on the driving state of the work vehicle 100 and the position of the detected obstacle in the storage device 650, and adds it to the driving history of the work vehicle 100. The processing device 660 may also add information other than information on the position of the detected obstacle to the driving history of the work vehicle 100. The information added to the driving history of the work vehicle 100 is used for subsequent driving control of the work vehicle 100, and the accuracy of driving control of the work vehicle 100 is improved.

[0151] If it is determined in step S202 that the detected obstacle candidate is not an obstacle, the process proceeds to step S207.

[0152] In step S207, the processing device 660 determines whether an obstacle candidate has been detected in the past at the same position or a nearby position by referencing the travel history of the work vehicle 100 stored in the storage device 650. If it is determined in step S207 that an obstacle candidate has been detected in the past at the same position or a nearby position, the process proceeds to step S208. If it is determined in step S207 that an obstacle candidate has not been detected in the past at the same position or a nearby position, the process proceeds to step S210.

[0153] In step S208, the processing device 660 determines whether or not an obstacle candidate previously detected at the same position or a nearby position was an obstacle by referencing the travel history of the work vehicle 100 stored in the storage device 650. If the processing device 660 determines in step S208 that an obstacle candidate previously detected at the same position or a nearby position was an obstacle, the processing device 660 proceeds to step S204.

[0154] By performing the processes of steps S207 and S208, even if an object detected as an obstacle candidate is determined not to be an obstacle (e.g., weeds, a small amount of snow, a small puddle, etc.), the processing device 660 can refer to the past driving history of the work vehicle 100 and, for example, if the storage device 650 stores information about an obstacle (e.g., unevenness in the ground) that was previously detected and avoided at the same or a nearby position, it is possible to prevent an obstacle hidden by the detected object (e.g., unevenness in the ground (sinkhole, defect, protrusion, etc.) hidden by weeds or snow) from being overlooked. In particular, if an obstacle detected in the past is part of the ground in a specific state, it may be hidden by weeds, a small amount of snow, a small puddle, etc. and not be detected by the sensor data. Furthermore, if an obstacle detected in the past is a small, immobile object (e.g., a small stone, etc.), there is a high possibility that the same obstacle will be present in the same position, and may be hidden by weeds or snow. The processing device 660 can accurately detect these obstacles by having the work vehicle 100 perform an avoidance operation while referencing the travel history of the work vehicle 100 .

[0155] If it is determined in step S208 that the obstacle candidate previously detected at the same position or a nearby position was not an obstacle, the processing unit 660 proceeds to step S209.

[0156] In step S209, the processing device 660 increases the alert level for sensing the surrounding environment. The processing device 660, for example, causes the work vehicle 100 to continue traveling without changing the traveling state of the work vehicle 100. The processing device 660 may increase the alert level for sensing the surrounding environment and may also change the traveling state of the work vehicle 100 (for example, may reduce the speed of the work vehicle 100).

[0157] In step S210, the processing device 660 does not cause the work vehicle 100 to perform an avoidance operation. The processing device 660 may lower the alert level in sensing the surrounding environment. The processing device 660 causes the work vehicle 100 to continue traveling without changing the traveling state of the work vehicle 100.

[0158] The processing device 660 repeats the processes from step S201 to step S210 until a command to end the operation is issued (step S211).

[0159] Next, the procedure by which the processing device 660 causes the work vehicle 100 to perform an avoidance operation to avoid an obstacle (here, setting an alert level in sensing the surrounding environment) will be described with reference to Figure 17. Figure 17 is a flowchart showing another example of the procedure by which the processing device 660 causes the work vehicle 100 to perform an avoidance operation to avoid an obstacle. Detailed descriptions of steps common to the flowcharts of Figures 14 to 16 may be omitted.

[0160] In step S219, the processing device 660 determines whether or not an obstacle candidate was previously detected on the target route of the work vehicle 100 by referencing the driving history of the work vehicle 100 stored in the storage device 650. For example, the processing device 660 determines whether or not an obstacle candidate was previously detected on the target route of the work vehicle 100 within a preset distance Da from the current position of the work vehicle 100 (for example, within 70 m from the current position of the work vehicle 100). The value of distance Da can be adjusted depending on the driving state of the work vehicle 100 (for example, the speed of the work vehicle 100), the type of work vehicle 100, the type of work implement 300 coupled to the work vehicle 100, etc. The processing device 660 may perform the determination of whether or not an obstacle candidate was previously detected on the target route of the work vehicle 100 multiple times. For example, it is determined whether or not an obstacle candidate has been detected in the past on the target route of the work vehicle 100 within a preset distance Da from the current position of the work vehicle 100, and if it is determined that an obstacle candidate has not been detected, it is determined whether or not an obstacle candidate has been detected in the past on the target route of the work vehicle 100 within a preset distance Db from the current position of the work vehicle 100 (for example, within 25 m from the current position of the work vehicle 100). Distance Db is smaller than distance Da.

[0161] If it is determined in step S219 that an obstacle candidate has been previously detected on the target route of work vehicle 100, the process proceeds to step S220. If it is determined in step S219 that an obstacle candidate has not been previously detected on the target route of work vehicle 100, the processing device 660 does not cause work vehicle 100 to perform an avoidance operation (step S226). In other words, the processing device 660 does not increase the alert level of work vehicle 100, but rather maintains or lowers the alert level.

[0162] In step S220, the processing device 660 determines whether an obstacle candidate previously detected on the target route of the work vehicle 100 was determined to be an obstacle by referring to the driving history of the work vehicle 100 stored in the storage device 650. The processing device 660 can, for example, refer to the driving history of the work vehicle 100 stored in the storage device 650 and determine whether an obstacle candidate detected in the past was determined to be an obstacle based on the driving state of the work vehicle 100 when the obstacle candidate was previously detected. For example, if the work vehicle 100 was traveling on a route that avoids the obstacle candidate, the obstacle candidate is considered to have been determined to be an obstacle. If the driving history of the work vehicle 100 stored in the storage device 650 includes information on the results of identifying the type of obstacle candidate detected in the past, the processing device 660 may determine whether the obstacle candidate was determined to be an obstacle based on the type of the obstacle candidate. For example, if an obstacle candidate is identified as a moving object, it may be determined to be an obstacle.

[0163] If it is determined in step S220 that the obstacle candidate previously detected on the target route of work vehicle 100 has been determined to be an obstacle, the process proceeds to step S221. If it is determined in step S220 that the obstacle candidate previously detected on the target route of work vehicle 100 has been determined to be a non-obstacle, the process proceeds to step S225.

[0164] In step S221, the processing device 660 determines whether an obstacle detected in the past is likely to exist at the same position at present by referring to the travel history of the work vehicle 100 stored in the storage device 650. For example, if an obstacle detected in the past was identified as an immobile object, the processing device 660 may determine that it is likely to exist at the same position at present.

[0165] If it is determined in step S221 that the obstacle detected in the past may still be present in the same position, the process proceeds to step S222. If it is determined in step S221 that the obstacle detected in the past does not still exist in the same position, the process proceeds to step S224.

[0166] In step S222, the processing device 660 acquires data on the driving state of the work vehicle 100 corresponding to obstacles detected in the past from the storage device 650, and changes the driving state of the work vehicle 100 based on the acquired data.

[0167] After step S222, in step S223, the processing device 660 stores the traveling state of the work vehicle 100 in the storage device 650 and adds it to the traveling history of the work vehicle 100.

[0168] In steps S224 and S225, the processing device 660 increases the alert level for sensing the surrounding environment. The processing device 660, for example, causes the work vehicle 100 to continue traveling without changing the traveling state of the work vehicle 100. The processing device 660 may increase the alert level for sensing the surrounding environment and may also change the traveling state of the work vehicle 100 (for example, may reduce the speed of the work vehicle 100).

[0169] The processing device 660 repeats the processes from step S219 to step S226 until a command to end the operation is issued (step S227).

[0170] In the example described here, the processing device 660 causes the work vehicle 100 to perform an avoidance operation to avoid an obstacle, based on sensor data acquired by one or more sensing devices possessed by the work vehicle 100 and the driving history of the work vehicle 100 stored in the storage device 650. However, the work vehicle 100 may also perform autonomous driving while sensing the surrounding environment using a sensing device possessed by, for example, agricultural machinery other than the work vehicle 100 or another mobile body such as a drone (unmanned aerial vehicle: UAV). The cruise control system according to this embodiment may determine the avoidance operation to be performed by the work vehicle 100 using sensor data acquired by a sensing device possessed by, for example, agricultural machinery other than the work vehicle 100 or another mobile body such as a drone.

[0171] Additionally, while an example has been described in which the processing device 660 of the management device 600 functions as a processing device of the cruise control system, some or all of the processing executed by the processing device 660 of the management device 600 in the cruise control system may be executed by another device. Such another device may be any of the terminal device 400 (processor 460), the control device 180 of the work vehicle 100, and the operation terminal 200. For example, if some of the processing executed by the processing device 660 of the management device 600 is executed by the control device 180, the combination of the management device 600 and the control device 180 functions as a processing device of the cruise control system. Additionally, in the example described, the storage device 650 of the management device 600 functions as a storage device that stores the travel history of the work vehicle 100, but the storage device 450 of the terminal device 400 and / or the storage device 170 of the work vehicle 100 may function as a storage device of the cruise control system. The work vehicle 100 may be equipped with a cruise control system, in which case the control device 180 and storage device 170 of the work vehicle 100 function as a processing device and storage device of the cruise control system. In addition to the ECUs 181 to 186 illustrated above, the control device 180 of the work vehicle 100 may further include an ECU for performing some or all of the processing for controlling traveling.

[0172] As described above, the present disclosure includes the travel control system, agricultural machine, and travel control method described in the following items.

[0173] [Item 1] A travel control system for an agricultural machine that performs automatic driving while sensing the surrounding environment, comprising: a storage device that stores a travel history including information on the past travel state of the agricultural machine and the positions of obstacle candidates detected during travel; and a processing device that causes the agricultural machine to perform an avoidance operation to avoid obstacles based on sensor data obtained by sensing the surrounding environment and the travel history.

[0174] [Item 2] The driving control system according to Item 1, wherein the processing device, when an obstacle candidate is detected based on the sensor data, changes the driving state of the agricultural machine and / or increases an alert level in sensing the surrounding environment based on the sensor data and the driving history.

[0175] [Item 3] The driving control system according to Item 2, wherein when an obstacle candidate is detected based on the sensor data and the driving state of the agricultural machine is changed based on the sensor data and the driving history, the processing device adds the position where the obstacle candidate was detected and the changed driving state of the agricultural machine to the driving history.

[0176] [Item 4] The driving control system according to any one of items 1 to 3, wherein the processing device sets an alert level for sensing the surrounding environment based on the driving history while the agricultural machine is traveling.

[0177] [Item 5] The cruise control system according to any one of items 1 to 4, wherein the processing device, when increasing an alert level in sensing the surrounding environment, increases the amount of sensor data by increasing the frequency of acquiring the sensor data or widening the range of the surrounding environment from which the sensor data is acquired.

[0178] [Item 6] The driving control system according to any one of Items 1 to 5, wherein the driving history further includes at least one of information on the type of obstacle candidate detected during past driving of the agricultural machine, information on the size of the obstacle candidate, information on whether the obstacle candidate was determined to be an obstacle, information on the date and time when the obstacle candidate was detected, and information on the weather when the obstacle candidate was detected.

[0179] [Item 7] The cruise control system according to any one of items 1 to 6, wherein when an obstacle candidate is detected based on the sensor data, the processing device identifies a type of the detected obstacle candidate.

[0180] [Item 8] The driving control system according to any one of Items 1 to 7, wherein the processing device detects an obstacle candidate based on the sensor data, and when the detected obstacle candidate is a moving object, stops the agricultural machine for a predetermined time, senses the surrounding environment after the predetermined time has elapsed, and when no obstacle candidate is detected, resumes driving of the agricultural machine.

[0181] [Item 9] The cruise control system according to any one of Items 1 to 8, wherein the processing device detects an obstacle candidate based on the sensor data, and when the detected obstacle candidate is a stationary object, generates or changes a target path for the agricultural machine so as to avoid the detected obstacle candidate.

[0182] [Item 10] The driving control system according to any one of items 1 to 9, wherein the processing device, when an obstacle candidate is detected based on the sensor data, acquires information on a position where the detected obstacle candidate is located, and, if the driving history includes information on an obstacle candidate previously detected at the position, causes the agricultural machine to perform the avoidance operation using data on the driving state of the agricultural machine included in the driving history.

[0183] [Item 11] The driving control system according to any one of items 1 to 10, wherein the processing device, while the agricultural machine is traveling, increases an alert level in sensing of the surrounding environment if the traveling history includes information on obstacle candidates previously detected on the target route of the agricultural machine, and maintains the alert level in sensing of the surrounding environment if the traveling history does not include information on obstacle candidates previously detected on the target route of the agricultural machine.

[0184] [Item 12] The driving control system according to any one of Items 1 to 11, wherein, while the agricultural machine is traveling, if the traveling history includes information on obstacle candidates that have been previously detected on the target route of the agricultural machine, the processing device sets a higher alert level for sensing the surrounding environment than if the traveling history does not include information on obstacle candidates that have been previously detected on the target route of the agricultural machine.

[0185] [Item 13] An agricultural machine equipped with the travel control system according to any one of items 1 to 12.

[0186] [Item 14] The agricultural machine according to Item 13, wherein, when the agricultural machine has a work vehicle and a work implement coupled to the work vehicle, the processing device changes the avoidance operation based on the type of the work implement and / or the width of the work implement.

[0187] [Item 15] A travel control method for an agricultural machine that performs automatic driving while sensing the surrounding environment, the travel control method comprising: storing a travel history including information on the past travel state of the agricultural machine and the positions of obstacle candidates detected during travel; and causing the agricultural machine to perform an avoidance operation to avoid obstacles based on sensor data obtained by sensing the surrounding environment and the travel history.

[0188] The technology disclosed herein can be applied to a driving control system that controls the automated driving of agricultural machinery such as a tractor, a harvester, a rice transplanter, a riding tiller, a vegetable transplanter, a brush cutter, a seed sowing machine, a fertilizer applicator, or an agricultural robot, an agricultural machine equipped with such a driving control system, and a driving control method.

[0189] 40: Obstacle, 50: GNSS satellite, 60: Reference station, 70: Field, 72: Work area, 74: Headland, 76: Road, 80: Network, 100: Work vehicle, 101: Vehicle body, 102: Prime mover (engine), 103: Transmission, 104: Wheels, 105: Cabin, 106: Steering device, 107: Driver's seat, 108: Coupling device, 110: Positioning device (GNSS unit), 111: GNSS receiver, 112: RTK receiver, 115: Inertial measurement unit (IMU), 116: Processing circuit, 120: Camera, 130: Obstacle sensor, 140: LiDAR sensor, 150: Sensor group, 152: Steering wheel sensor, 154 . . turning angle sensor, 156 . . . axle sensor, 160 . . . control system, 170 . . . storage device, 180 . . . control device, 181 to 186 . . . ECU, 190 . . . communication device, 200 . . . operation terminal, 210 . . . operation switch group, 220 . . . buzzer, 240 . . . drive device, 300 . . . work machine (implement), 340 . . . drive device, 380 . . . control device, 390 . . . communication device, 400 . . . terminal device, 420 . . . input device, 430 . . . display device, 450 . . . storage device, 460 . . . processor, 470 . . ROM, 480 . . . RAM, 490 . . . communication device, 600 . . . management device, 650 . . . storage device, 660 . . . processor (processing device), 670 . . . ROM, 680 . . . RAM, 690 . . . communication device

Claims

1. A driving control system for an agricultural machine that performs automatic driving while sensing the surrounding environment, a storage device that stores a travel history including information on the past travel state of the agricultural machine and the positions of obstacle candidates detected during travel; a processing device that causes the agricultural machine to perform an avoidance operation to avoid an obstacle based on sensor data obtained by sensing the surrounding environment and the travel history; A driving control system having the above configuration.

2. The processing device includes:

2. The driving control system according to claim 1, wherein when an obstacle candidate is detected based on the sensor data, the driving state of the agricultural machine is changed and / or an alert level in sensing of the surrounding environment is increased based on the sensor data and the driving history.

3. The processing device includes:

3. The driving control system according to claim 2, wherein when an obstacle candidate is detected based on the sensor data and the driving state of the agricultural machine is changed based on the sensor data and the driving history, the position where the obstacle candidate was detected and the changed driving state of the agricultural machine are added to the driving history.

4. The processing device includes: The travel control system according to claim 1 , further comprising: a monitoring level for sensing the surrounding environment based on the travel history while the agricultural machine is traveling.

5. The processing device includes:

4. The driving control system according to claim 1, wherein when an alert level in sensing the surrounding environment is increased, the amount of sensor data is increased by increasing the frequency of acquisition of the sensor data or widening the range of the surrounding environment from which the sensor data is acquired.

6. The driving history is 4. The driving control system according to claim 1, further comprising at least one of information on a type of obstacle candidate detected during past traveling of the agricultural machine, information on a size of the obstacle candidate, information on whether the obstacle candidate was determined to be an obstacle, information on a date and time when the obstacle candidate was detected, and information on the weather when the obstacle candidate was detected.

7. The processing device includes: The cruise control system according to claim 1 , further comprising: a detecting unit configured to detect an obstacle candidate based on the sensor data and to identify a type of the detected obstacle candidate.

8. The processing device includes:

4. The travel control system according to claim 1, wherein when an obstacle candidate is detected based on the sensor data and the detected obstacle candidate is a moving object, the agricultural machine is stopped for a predetermined time, the surrounding environment is sensed after the predetermined time has elapsed, and when no obstacle candidate is detected, the agricultural machine is caused to resume traveling.

9. The processing device includes:

4. The travel control system according to claim 1, wherein, when an obstacle candidate is detected based on the sensor data and the detected obstacle candidate is a stationary object, a target route of the agricultural machine is generated or changed so as to avoid the detected obstacle candidate.

10. The processing device includes: When an obstacle candidate is detected based on the sensor data, information on a position where the detected obstacle candidate is present is acquired; 4. The driving control system according to claim 1, wherein, when the driving history includes information on an obstacle candidate previously detected at the position, data on the driving state of the agricultural machine included in the driving history is used to cause the agricultural machine to perform the avoidance operation.

11. The processing device includes: While the agricultural machine is traveling, When the travel history includes information of an obstacle candidate that has been detected in the past on a target route of the agricultural machine, an alert level for sensing the surrounding environment is increased; maintain an alert level in sensing the surrounding environment when the travel history does not include information of an obstacle candidate that has been detected in the past on the target route of the agricultural machine; The cruise control system according to any one of claims 1 to 3.

12. The processing device includes: While the agricultural machine is traveling, When the travel history includes information on an obstacle candidate that has been detected in the past on the target route of the agricultural machine, an alert level for sensing the surrounding environment is set higher than when the travel history does not include information on an obstacle candidate that has been detected in the past on the target route of the agricultural machine. The cruise control system according to any one of claims 1 to 3.

13. An agricultural machine comprising the travel control system according to any one of claims 1 to 3.

14. When the agricultural machine has a work vehicle and a work implement connected to the work vehicle, The agricultural machine according to claim 13, wherein the processing device changes the avoidance operation based on the type of the implement and / or the width of the implement.

15. A travel control method for an agricultural machine that performs automatic driving while sensing a surrounding environment, comprising: storing a travel history including information on past travel states of the agricultural machine and positions of obstacle candidates detected during travel; causing the agricultural machine to perform an avoidance operation to avoid an obstacle based on sensor data obtained by sensing the surrounding environment and the travel history; and A driving control method comprising: