Abstract: Disclosed are autonomous work vehicles, adjustable implement systems, and autonomous vehicle systems for executing an operational adjustment of the vehicle and/or an attached implement during an operational task. The operational adjustment may be performed upon detecting a value of an operational variable that lies outside an operational envelope. The operational adjustment may comprise adjusting an operational parameter of the vehicle (e.g., velocity, heading, engine torque, etc.) and/or the implement (e.g., implement position) to a desired setting. The desired setting may be configured to return the operational variable to a value within the operational envelope. Doing so may reduce or prevent harm to the implement, and/or to increase the effectiveness of the operational task. Detecting a value of the operational variable lying outside the operational envelope may induce the autonomous work vehicle to send an abnormal condition notification to an operator.

Abstract: Disclosed are autonomous work vehicles and methods for locally confirming the presence of obstacles within a hazard zone extending over an area near and/or adjacent to the autonomous work vehicle. The method may enable the autonomous work vehicle to resume autonomous operation after losing perception of the operating environment. The method may include activating an emergency stop (e-stop) mode after perceptual loss, entering a manual mode upon receiving first operator input, receiving second operator input indicating that no obstacles were found within the hazard zone, clearing a hazard grid after receiving second operator input, and activating an autonomous mode for autonomous operation of the vehicle. The method may include re-activating the e-stop mode and/or manual mode when the vehicle detects an obstacle within the hazard zone during autonomous operation, when too much time elapses between the first and second operator input, or when the clearing the hazard grid fails.

Abstract: An autonomous vehicle may include a sensor array; an engine output control system; a braking control system; and a controller. The controller may be communicatively coupled with the sensor array, the engine output control system, and the braking control system. The controller may be configured to: sense an environment with the sensor array to produce sensor data; receiving autonomous vehicle state data; determining whether the autonomous vehicle state data is above a threshold state value; in the event the autonomous vehicle state data is above a threshold state value, not using the sensor data to operate the autonomous vehicle; and in the event the autonomous vehicle state data is not above a threshold state value, using the sensor data to operate the autonomous vehicle.

Abstract: Systems and methods for determining the order of autonomous vehicles proceeding through the intersection locally by the autonomous vehicles are disclosed. The method may include determining the presence of overlapping regions between paths of different autonomous vehicles. The method may also include assigning one of the autonomous vehicles as the authority to decide which autonomous vehicle should proceed through the intersection before all other autonomous vehicles.

Abstract: A simulation system is disclosed for generating a validation point cloud for validating a lidar processing algorithm. It comprises a simulation digital storage medium comprising lidar data from a lidar sensor and a three-dimensional model of at least one object, wherein the lidar data comprises an origin and a point cloud; and a controller in communication with the simulation digital storage medium.

Abstract: Disclosed are autonomous work vehicles, systems, and methods for implementing a pattern on a surface of a work area according to a predetermined design, scheme, or plan. The method employed by the autonomous work vehicle may include generating a plurality of paths, generating a combined path by linking the plurality of paths, and instructing the autonomous work vehicle to follow the combined path while interacting with a surface of the work area to implement the pattern. The plurality of paths may include one or more external paths, one or more internal paths, a plurality of swath paths, and a plurality of connection paths that connect the one or more external paths, internal paths, and swath paths together. Generating the combined path may also include determining any untraced areas of the work area and tracing these areas with patch paths that may then be linked with the combined path.

Abstract: Disclosed are systems and methods for safely navigating an autonomous work vehicle by instructing the autonomous work vehicle to provide measured responses according to the proximity of an obstacle to the autonomous work vehicle. The response of the autonomous work vehicle may be determined based on a response set associated with one or more zones disposed around the autonomous work vehicle or about a selected navigational path. The response set may include warnings, such as activation of lights and/or alarms of the autonomous work vehicle, and/or motor actions, including reducing a velocity of the autonomous work vehicle, selecting another navigational path to avoid the obstacle, and/or adjusting the activation and/or position of an implement connected to the autonomous work vehicle. A response may be initiated when visual sensor signal data associated with a zone indicates the presence of an obstacle within the zone.

Abstract: Disclosed are autonomous vehicle systems and methods for distinguishing atmospheric phenomena, such as dust clouds, from other objects within an operating environment. Point cloud data received from LiDAR sensors may be classified based on multiple returns from LiDAR sensor beams. Atmospheric phenomena may be classified based on a mean return number or a mean last return ratio, or other return-based parameters, for example, by comparison to a return threshold or through a classification process, such as a machine learning process. The autonomous vehicle may then be driven according to the classification of the point cloud data.

Abstract: Disclosed are autonomous vehicles, systems, and methods that rely on the fusing of primary and secondary sensor signal data for navigating an autonomous vehicle. The primary sensor may have greater detection capabilities than the secondary sensor, such as a primary sensor comprising a 3D LiDAR sensor and a secondary sensor comprising a 2D LiDAR sensor. The autonomous vehicle may rely on the primary sensor signal data to the exclusion of at least some of the secondary sensor signal data. The autonomous vehicle may prioritize the primary sensor signal data whenever the secondary sensor signal data conflicts with the primary sensor signal data. In some embodiments, the secondary sensor signal data may be used to increase, but not decrease, an occupancy probability of a cell used to navigate the autonomous vehicle. In some embodiments, the secondary sensor signal data may be scaled and adjusted with the primary sensor signal data.

Abstract: An autonomous vehicle is disclosed that includes a sensor array; an obstacle detection system configured to receive sensor data from the sensor array, a controller, a user interface configured to receive visual data from the camera system and to display the visual data from the camera system to an operator. The obstacle detection system may be configured to detect the presence of an obstacle based on the sensor data, and send out an inhibit signal to inhibit movement or start-up of the autonomous vehicle or to shut down the autonomous vehicle when an obstacle is detected. The controller may be configured to receive commands from an operator identifying or negating the presence of an obstacle; if the controller receives a command negating the presence of an obstacle, overriding the obstacle detection system and enabling movement of the autonomous vehicle.

Abstract: An autonomous work vehicle is disclosed that includes a vehicle control unit; a location determining system; a digital storage medium comprising map data defining: a map comprising a path for the autonomous work vehicle to travel, at least one error polygon defined around the autonomous work vehicle within the map; and a controller in communication with the digital storage medium, location determining system, and the vehicle control unit, wherein the controller performs an off path error check in which the controller: retrieves the map data from the digital storage medium; receives current location data from the location determining system; locates the autonomous work vehicle on the map with the location data; determines whether the path is within the error polygon around the autonomous work vehicle; and if the path is not within the error polygon, the vehicle control unit performs a corrective action on the autonomous work vehicle.

Abstract: Disclosed are autonomous vehicles and processes for verifying LiDAR signal data before relying on the visual sensors to navigate the autonomous vehicle through the operating environment and autonomous vehicles and processes for accurately determining implement position and/or orientation before continuing to operate the implement within the operating environment. The LiDAR signal data may be verified by looking for expected noise in the signal data, by observing expected movement of an implement, or by comparing an odometry dataset based on the LiDAR signal data to GPS and/or wheel speed data of the autonomous vehicle. The implement may be autonomously operated based on an implement sensor signal that may be verified by comparing implement sensor signal data to corresponding LiDAR signal data (e.g., when adjusting the position and/or orientation of the implement) or based on primarily relying on the LiDAR signal data to confirm the position and/or orientation of the implement.

Abstract: Disclosed are systems and methods for operating an autonomous vehicle. Specifically, disclosed are systems and methods for updating the occlusion probability associated with a plurality of cells within an occlusion probability map generated by the sensor field of view. The method comprises propagating a previous occlusion probability associated with a cell to the current occlusion probability, even if non-occluded sensor data is not available, if an elapsed time between attempted observation of the cell and successful observation of the cell is within a time threshold. The method may enable the autonomous vehicle to continue at operational efficiency despite occlusion of the one or more sensors used to navigate the vehicle.

Abstract: An autonomous vehicle is disclosed. The autonomous vehicle may include an operator seat, an occupancy system to capture occupancy data, wherein an operator volume is in a field of view of the occupancy system, the operator volume being defined as a volume above the operator seat in which an operator may or may not be seated, a vehicle control unit for controlling movement of the autonomous vehicle, a digital storage comprising field-of-view data representing the field of view of the occupancy system; and an operator detection system which receives field-of-view data and occupancy data; identifies occupancy points within the operator volume; and if the total number of occupancy points in the operator volume exceeds an occupancy threshold for a predetermined period of time, determining that an operator is located in the operator seat; and sending a stop signal to the vehicle control unit to stop the autonomous vehicle.

Abstract: An autonomous work vehicle is disclosed that includes a Lidar sensor and a vehicle control unit in communication with the Lidar sensor. The vehicle control unit receives Lidar data comprising a point cloud including a plurality of points from the Lidar sensor; classify points from the point cloud into ground points and non-ground points, wherein the ground points represent the ground, and the non-ground points represent non-ground objects; estimate a height map based on the classified ground points; determine object features based on the non-ground points, the object features including at least one of a size of an object, a height of the object, a lowest point of the object, and a slope of a terrain under an object based on the classified ground and non-ground points and the estimated height map; classify the object as a hazard or non-hazard based on the determined object features.

Abstract: An autonomous vehicle is disclosed that includes a camera system; a controller; and an obstacle detection system in communication with the camera system. The obstacle detection system, for example, may be configured to receive visual data from the camera system; and calculate a probability of an obstacle being present based on the visual data. The controller, for example, may be configured to determine the presence of an obstacle if a calculated probability in the obstacle detection system is above a probability threshold; and send an inhibit signal to stop or inhibit movement of the autonomous vehicle.

Abstract: An autonomous work vehicle may include a steering control system, a speed control system, and a sensor array. One or more processors may be communicatively coupled with the sensor array, the steering control system, and the speed control system. One or more computer-readable media may store instructions that cause the processors to identify a reference line within a map of two unconnected work areas. The processors may determine a swath angle and create a plurality of swaths based on the swath angle relative to the reference line. Alternating swaths may represent opposite directions. The plurality of swaths may overlap the two unconnected work areas within the map. The processors may trim the plurality of swaths based on geometric shape of the two unconnected work areas and create a path from the plurality of swaths.

Abstract: Disclosed are autonomous vehicles and processes for verifying LiDAR signal data before relying on the visual sensors to navigate the autonomous vehicle through the operating environment and autonomous vehicles and processes for accurately determining implement position and/or orientation before continuing to operate the implement within the operating environment. The LiDAR signal data may be verified by looking for expected noise in the signal data, by observing expected movement of an implement, or by comparing an odometry dataset based on the LiDAR signal data to GPS and/or wheel speed data of the autonomous vehicle. The implement may be autonomously operated based on an implement sensor signal that may be verified by comparing implement sensor signal data to corresponding LiDAR signal data (e.g., when adjusting the position and/or orientation of the implement) or based on primarily relying on the LiDAR signal data to confirm the position and/or orientation of the implement.

Abstract: An electronic device for an autonomous vehicle is provided. The electronic device includes a processor and a memory storing instructions that, when executed, cause the processor to acquire sensor data from a sensor array, detect one or more objects based on the sensor data, and generate virtual boundaries for the detected objects. The processor analyzes predefined physical attributes to distinguish between permanent and temporary obstacles, selectively identifies permanent obstacles for inclusion in a digital map and transforms virtual boundaries into geo-referenced coordinates. The geo-referenced coordinates are transmitted to a base station based on predefined triggers for incorporation into the digital map used by the autonomous vehicle for navigation.

Abstract: Disclosed are autonomous work vehicles and adjustable implement systems for automatically and/or autonomously adjusting the position of an implement. The implement may be adjusted by receiving implement data from a sensor array, determining a position of the implement based on implement data, and instructing the implement control system to adjust the position of the implement to a desired position. The sensor array may include a tractor engine torque sensor, a wheel speed sensor, and/or a LiDAR sensor. The implement may be connected to a hydraulic cylinder. The implement data may indicate parameters of the implement, including timing data and/or flow data indicating the flow rate of hydraulic fluid in or out of the hydraulic cylinder. The extension of hydraulic cylinder may be determined based on the timing data and/or the flow data and position of the implement may then be determined based on the extension of the hydraulic cylinder.