Autonomous Solutions, Inc.

Cnh Industrial America LLC

Cnh Industrial Canada, Ltd.

Cnh Industrial Belgium Nv

Taylor Chad Bybee

John Henry Posselius

Bret Todd Turpin

Luca Ferrari

James Wayne Henry

Christopher Alan Foster

Daniel John Morwood

Nathan Eric Bunderson

Jeffrey L Ferrin

Brett Carson McClelland

Michael G Hornberger

Robert Dean Ashby

Chad Brian Jeppesen

Brad Abram Baillio

James Patrick Yonk

Gary Ellingson

Lyle Shawn Frisby

Joshua Vanfleet

Bryant Chandler

Eli Taylor

Jaron Haight

Jeff Ferrin

Garrett Winward

Nate Bunderson

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.

Truck load dumping for an autonomous loader

Some embodiments of the invention include a method for updating an occlusion probability map. An occlusion probability map represents the probability that a given portion of the sensor field is occluded from one or more sensors. In some embodiments, a method may include receiving field of view data from a sensor system; producing a probabilistic model of the sensor field of view; and updating an occlusion probability map using the probabilistic model and field of view data.

Point cloud occlusion mapping for autonomous vehicles

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.

Localization system for autonomous vehicles using sparse radar data

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.

Semantic information sharing in autonomous vehicles

In one aspect, a system for illuminating a field of view of a vision-based sensor mounted on an agricultural machine may include an agricultural machine having a vision-based sensor. The system may also include a light source configured to emit supplemental light to illuminate at least a portion of the field of view of the vision-based sensor. Furthermore, the system may include a controller communicatively the light source. The controller may configured to control an operation of the light source based on an input indicative of ambient light present within the field of view of the vision-based sensor.

System and method for illuminating the field of view of a vision-based sensor mounted on an agricultural machine

A swath tracking system for an off-road vehicle includes a control system with a processor and a memory. The control system is configured to receive a plurality of vehicle location points and a current vehicle state, wherein the current vehicle state comprises a current vehicle location, generate a planned vehicle path through one or more of the plurality of vehicle location points, generate a correction path from the current vehicle location to a point along the planned vehicle path ahead of the current vehicle location along a direction of travel, generate a blended path by blending the planned vehicle path and the correction path based at least in part on an assigned weight, wherein the assigned weight is based at least in part on a heading error, a distance between the current vehicle location and the planned path, or a combination thereof, and guide the off-road vehicle along the blended path.

Path planning system for autonomous off-road vehicles

The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned crop residue classification model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.

Measuring crop residue from imagery using a machine-learned classification model

One or more tangible, non-transitory, machine-readable media including instructions that cause a processor to receive a signal indicative of a residue coverage on a surface of an agricultural field from a sensor, and the sensor is positioned behind a harvester system relative to a direction of travel. The instructions also cause the processor to determine the residue coverage on the surface of the agricultural field based on the signal, and the residue coverage includes a percentage of the agricultural field that is covered by residue. Further, the instructions cause the processor to control a residue control system based on the residue coverage.

System and method for monitoring residue output from a harvester

In one aspect, a system for adjusting a sampling rate of a sensor mounted on an agricultural machine may include an agricultural machine and a sensor mounted on the agricultural machine, with the sensor being configured to capture data at a sampling rate. The system may also include a controller communicatively coupled to the sensor. The controller may be configured to receive an input indicative of an operational parameter of the agricultural machine and adjust the sampling rate at which the sensor captures data based on the received input.

System and method for adjusting the sampling rate of a sensor mounted on an agricultural machine

The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned crop residue classification model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. Furthermore, principal components analysis, such as projecting image patches onto Eigen-images, can be performed to reduce the dimensionality of the feature vector provided to the classification model.

Measuring crop residue from imagery using a machine-learned classification model in combination with principal components analysis

The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned semantic segmentation model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.

Measuring crop residue from imagery using a machine-learned semantic segmentation model

The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned convolutional neural network to determine a level of crop residue for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.

Measuring crop residue from imagery using a machine-learned convolutional neural network

The present disclosure provides systems and methods for adjusting the output of a field measurement system to conform to agronomy measurements. In particular, the present subject matter is directed to a calibration process and system that uses a calibration model to convert field measurement data expressed according to an automatic system metric into agronomy data that is expressed according to an agronomy metric.

Systems and methods for adjusting the output of a field measurement system to conform to agronomy measurements

In one aspect, a system for monitoring sensor performance on an agricultural machine may include a controller configured to receive a plurality of images from the vision-based sensor mounted on an agricultural machine. The controller may be configured to determine an image parameter value associated with each of a plurality of pixels contained within each of the plurality of images. For each respective pixel of the plurality of pixels, the controller may be configured to determine a variance associated with the image parameter values for the respective pixel across the plurality of images. Furthermore, when the variance associated with the image parameter values for a given pixel of the plurality of pixels falls outside of a predetermined range, the controller may be configured to identify the given pixel as being at least one of obscured or inoperative.

System and method for monitoring sensor performance on an agricultural machine

A method for automatically monitoring soil surface roughness as a ground-engaging operation is being performed within a field may include receiving pre-operation surface roughness data associated with a given portion of the field and receiving post-operation surface roughness data associated with the given portion of the field. In addition, the method may include analyzing the pre-operation and post-operation surface roughness data to determine a surface roughness differential associated with the performance of the ground-engaging operation and actively adjusting the operation of at least one of an associated work vehicle and/or implement when the surface roughness differential differs from a target set for the surface roughness differential.

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Logan, UT, United States of America

Taylor Chad Bybee