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 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: 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: 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 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: 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: Systems and methods are disclosed for a remote operator to observe and rectify safety issues on an autonomous work vehicle. For example, an autonomous work vehicle may detect an obstacle on or near a path within a work zone. The obstacle may be detected using a camera, a LiDAR or a RADAR. The autonomous work vehicle may stop prior to contacting the object. The autonomous work vehicle may also image the obstacle and send the image to a mobile device either directly or via a remote server. The user may determine the obstacle is not a safety issue or remove the obstacle from the path. In either case, the user may send an indication through the user interface that the path is clear and the autonomous work vehicle is safe to proceed along the path.

Abstract: An autonomous vehicle system and method is disclosed that allows an operator to override a stop command issued from an obstacle detection and avoidance subsystem of the autonomous vehicle. Most autonomous vehicles include an obstacle detection and avoidance subsystem that detects potential obstacles along the path of the autonomous vehicle and may send a command to stop the autonomous vehicle. When the obstacle detection and avoidance subsystem detects a potential obstacle (regardless of whether the potential obstacle does or does not exist) and the path is still traversable, the autonomous vehicle may still stop unnecessarily. The disclosed system and methods will allow an operator to override commands issued by the obstacle detection and avoidance subsystem and allow the autonomous vehicle to continue along the path regardless of any potential obstacle.

Abstract: Systems and methods are discussed to direct an autonomous loader to a dump truck. In some embodiments, a method may include receiving dump truck geolocation data for a dump truck; receiving loader geolocation data for an autonomous loader; raising a bucket on the autonomous loader to a height; directing the autonomous loader toward the dump truck using the loader geolocation data and the dump truck geolocation data so that the bucket is positioned above a body or bed of the dump truck; and rotating the bucket downward to release a load in the bucket into the body or bed of dump truck.

Abstract: An autonomous vehicle system and method is disclosed that allows an operator to override a stop command issued from an obstacle detection and avoidance subsystem of the autonomous vehicle. Most autonomous vehicles include an obstacle detection and avoidance subsystem that detects potential obstacles along the path of the autonomous vehicle and may send a command to stop the autonomous vehicle. When the obstacle detection and avoidance subsystem detects a potential obstacle (regardless of whether the potential obstacle does or does not exist) and the path is still traversable, the autonomous vehicle may still stop unnecessarily. The disclosed system and methods will allow an operator to override commands issued by the obstacle detection and avoidance subsystem and allow the autonomous vehicle to continue along the path regardless of any potential obstacle.

Abstract: Systems and methods are discussed to direct an autonomous loader to a dump truck. In some embodiments, a method may include receiving dump truck geolocation data for a dump truck; receiving loader geolocation data for an autonomous loader; raising a bucket on the autonomous loader to a height; directing the autonomous loader toward the dump truck using the loader geolocation data and the dump truck geolocation data so that the bucket is positioned above a body or bed of the dump truck; and rotating the bucket downward to release a load in the bucket into the body or bed of dump truck.

Abstract: An autonomous vehicle is disclosed that includes a velocity sensor; a radar system; a digital storage medium; and a controller in communication with the digital storage medium, radar system, and the velocity sensor. The controller, for example, may retrieve map data from the digital storage medium; receive current radar data from the radar system; receive autonomous vehicle velocity data from the velocity sensor; identify radar data points in the current radar data that represent objects in motion; remove radar data points from the current radar data that represent objects in motion; and/or match the radar data with the map data to return location data.

Abstract: An autonomous vehicle is disclosed. The 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 are disclosed for a semantic information sharing within a group of autonomous ground vehicles. In some embodiments, a method may include sensing an environment near a first autonomous vehicle to produce first environmental data; creating a first mathematical model representing the environment near the first autonomous vehicle based on the first environmental data; sending a request for additional environmental data to a second autonomous vehicle; receiving a second mathematical model from the second autonomous vehicle; and merging the second mathematical model with the first mathematical model.