Abstract: Systems and methods for training and executing machine learning models to generate lane index values are disclosed. A method includes identifying a set of image data captured by at least one autonomous vehicle when the at least autonomous vehicle is positioned in a lane of a roadway and respective ground truth localization data; determining a plurality of lane index values for the set of image data based on the ground truth localization data; labeling the set of image data with the plurality of lane index values, the lane index values representing a number of lanes from a leftmost or rightmost lane to the lane in which the at least one autonomous vehicle was positioned; and training, using the labeled set of image data, a plurality of machine learning models that generate a left lane index value and a right lane index value as output.

Abstract: Systems and methods for training and executing machine learning models to generate lane index values are disclosed. A method includes identifying a set of image data captured by at least one autonomous vehicle when the at least autonomous vehicle is positioned in a lane of a roadway and respective ground truth localization data; determining a plurality of lane index values for the set of image data based on the ground truth localization data; labeling the set of image data with the plurality of lane index values, the lane index values representing a number of lanes from a leftmost or rightmost lane to the lane in which the at least one autonomous vehicle was positioned; and training, using the labeled set of image data, a plurality of machine learning models that generate a left lane index value and a right lane index value as output.

Abstract: Systems and methods for training and executing machine learning models to generate lane index values are disclosed. A method includes identifying a set of image data captured by at least one autonomous vehicle when the at least autonomous vehicle is positioned in a lane of a roadway and respective ground truth localization data; determining a plurality of lane index values for the set of image data based on the ground truth localization data; labeling the set of image data with the plurality of lane index values, the lane index values representing a number of lanes from a leftmost or rightmost lane to the lane in which the at least one autonomous vehicle was positioned; and training, using the labeled set of image data, a plurality of machine learning models that generate a left lane index value and a right lane index value as output.

Abstract: A skateboard deck that is both stiff in one dimension and flexible in another dimension is disclosed. The deck includes an elongated body characterized by a longitudinal axis, a plurality of ribs, and a plurality of apertures. The plurality of ribs are long, narrow beams oriented substantially parallel to the longitudinal axis and integral with the bottom of the elongated body. The plurality of apertures are distributed in a plurality of rows also oriented substantially parallel to the longitudinal axis. The ribs and rows of apertures are interposed in an alternating pattern across the width of the deck. The resulting deck is stiff in the longitudinal direction yet flexible with respect to rotation about the longitudinal axis. The deck is therefore flexible enough to enable the front and rear trucks to rotate with respect to one another while still being rigid enough to withstand the weight of the rider.

Abstract: A detection system used to alert an operator of a work machine of humans or objects dangerously close to the machine or a work tool attached to the machine. The detection system uses one or more cameras to capture images of an area surrounding the machine. The captured images are displayed on an interface electronically connected to a processor. Prior to operation, one or more zones surrounding the work tool or work machine are defined and projected on the images displayed on the interface. The processor analyzes the images captured by the cameras and determines if a characteristic of a predetermined object is within one or more of the identified zones. If the processor determines the characteristic of the predetermined object is within one of the zones, the processor will identify the object on the display and trigger a warning system to alert to the operator to take necessary precautions.

Abstract: A detection system used to alert an operator of a work machine of humans or objects dangerously close to the machine or a work tool attached to the machine. The detection system uses one or more cameras to capture images of an area surrounding the machine. The captured images are displayed on an interface electronically connected to a processor. Prior to operation, one or more zones surrounding the work tool or work machine are defined and projected on the images displayed on the interface. The processor analyzes the images captured by the cameras and determines if a characteristic of a predetermined object is within one or more of the identified zones. If the processor determines the characteristic of the predetermined object is within one of the zones, the processor will identify the object on the display and trigger a warning system to alert to the operator to take necessary precautions.

Abstract: A detection system used to alert an operator of a work machine of humans or objects dangerously close to the machine or a work tool attached to the machine. The detection system uses one or more cameras to capture images of an area surrounding the machine. The captured images are displayed on an interface electronically connected to a processor. Prior to operation, one or more zones surrounding the work tool or work machine are defined and projected on the images displayed on the interface. The processor analyzes the images captured by the cameras and determines if a characteristic of a predetermined object is within one or more of the identified zones. If the processor determines the characteristic of the predetermined object is within one of the zones, the processor will identify the object on the display and trigger a warning system to alert to the operator to take necessary precautions.

Abstract: A detection system used to alert an operator of a work machine of humans or objects dangerously close to the machine or a work tool attached to the machine. The detection system uses one or more cameras to capture images of an area surrounding the machine. The captured images are displayed on an interface electronically connected to a processor. Prior to operation, one or more zones surrounding the work tool or work machine are defined and projected on the images displayed on the interface. The processor analyzes the images captured by the cameras and determines if a characteristic of a predetermined object is within one or more of the identified zones. If the processor determines the characteristic of the predetermined object is within one of the zones, the processor will identify the object on the display and trigger a warning system to alert to the operator to take necessary precautions.

Abstract: A detection system used to alert an operator of a work machine of humans or objects dangerously close to the machine or a work tool attached to the machine. The detection system uses one or more cameras to capture images of an area surrounding the machine. The captured images are displayed on an interface electronically connected to a processor. Prior to operation, one or more zones surrounding the work tool or work machine are defined and projected on the images displayed on the interface. The processor analyzes the images captured by the cameras and determines if a characteristic of a predetermined object is within one or more of the identified zones% If the processor determines the characteristic of the predetermined object is within one of the zones, the processor will identify the object on the display and trigger a warning system to alert to the operator to take necessary precautions.