Abstract: In various examples, a machine learning model—such as a deep neural network (DNN)—may be trained to use image data and/or other sensor data as inputs to generate two-dimensional or three-dimensional trajectory points in world space, a vehicle orientation, and/or a vehicle state. For example, sensor data that represents orientation, steering information, and/or speed of a vehicle may be collected and used to automatically generate a trajectory for use as ground truth data for training the DNN. Once deployed, the trajectory points, the vehicle orientation, and/or the vehicle state may be used by a control component (e.g., a vehicle controller) for controlling the vehicle through a physical environment. For example, the control component may use these outputs of the DNN to determine a control profile (e.g., steering, decelerating, and/or accelerating) specific to the vehicle for controlling the vehicle through the physical environment.

Abstract: In various examples, a machine learning model—such as a deep neural network (DNN)—may be trained to use image data and/or other sensor data as inputs to generate two-dimensional or three-dimensional trajectory points in world space, a vehicle orientation, and/or a vehicle state. For example, sensor data that represents orientation, steering information, and/or speed of a vehicle may be collected and used to automatically generate a trajectory for use as ground truth data for training the DNN. Once deployed, the trajectory points, the vehicle orientation, and/or the vehicle state may be used by a control component (e.g., a vehicle controller) for controlling the vehicle through a physical environment. For example, the control component may use these outputs of the DNN to determine a control profile (e.g., steering, decelerating, and/or accelerating) specific to the vehicle for controlling the vehicle through the physical environment.

Abstract: In various examples, image space coordinates of an image from a video may be labeled, projected to determine 3D vehicle space coordinates, then transformed to 3D world space coordinates using known 3D world space coordinates and relative positioning between the coordinate spaces. For example, 3D vehicle space coordinates may be temporally correlated with known 3D world space coordinates measured while capturing the video. The known 3D world space coordinates and known relative positioning between the coordinate spaces may be used to offset or otherwise define a transform for the 3D vehicle space coordinates to world space. Resultant 3D world space coordinates may be used for one or more labeled frames to generate ground truth data. For example, 3D world space coordinates for left and right lane lines from multiple frames may be used to define lane lines for any given frame.

Abstract: In various examples, a plurality of poses corresponding to one or more configuration parameters within an environment—such as a location of a machine within an environment, an orientation of a machine within an environment, a sensor angle pose of a machine, or a sensor location of a machine—may be used to generate training data and corresponding ground truth data for training a machine learning model—such as a deep neural network (DNN). As a result, the machine learning model, once deployed, may more accurately compute one or more outputs—such as outputs representative of lane boundaries, trajectories for an autonomous machine, etc.—agnostic to machine and/or sensor poses of the machine within which the machine learning model is deployed.

Abstract: In various examples, rapid resolution of deep neural network (DNN) failure modes may be achieved by deploying patch neural networks (PNNs) trained to operate effectively on the failure modes of the DNN. The PNNs may operate on the same or additional data as the DNN, and may generate new signals in addition to those generated using the DNN that address the failure modes of the DNN. A fusion mechanism may be employed to determine which output to rely on for a given instance of the DNN/PNN combination. As a result, failure modes of the DNN may be addressed in a timely manner that requires minimal deactivation or downtime for the DNN, a feature controlled using the DNN, and/or semi-autonomous or autonomous functionality as a whole.

Abstract: In various examples, sensor data used to train an MLM and/or used by the MLM during deployment, may be captured by sensors having different perspectives (e.g., fields of view). The sensor data may be transformed—to generate transformed sensor data—such as by altering or removing lens distortions, shifting, and/or rotating images corresponding to the sensor data to a field of view of a different physical or virtual sensor. As such, the MLM may be trained and/or deployed using sensor data captured from a same or similar field of view. As a result, the MLM may be trained and/or deployed—across any number of different vehicles with cameras and/or other sensors having different perspectives—using sensor data that is of the same perspective as the reference or ideal sensor.

Abstract: In examples, image data representative of an image of a field of view of at least one sensor may be received. Source areas may be defined that correspond to a region of the image. Areas and/or dimensions of at least some of the source areas may decrease along at least one direction relative to a perspective of the at least one sensor. A downsampled version of the region (e.g., a downsampled image or feature map of a neural network) may be generated from the source areas based at least in part on mapping the source areas to cells of the downsampled version of the region. Resolutions of the region that are captured by the cells may correspond to the areas of the source areas, such that certain portions of the region (e.g., portions at a far distance from the sensor) retain higher resolution than others.

Abstract: In various examples, training sensor data generated by one or more sensors of autonomous machines may be localized to high definition (HD) map data to augment and/or generate ground truth data—e.g., automatically, in embodiments. The ground truth data may be associated with the training sensor data for training one or more deep neural networks (DNNs) to compute outputs corresponding to autonomous machine operations—such as object or feature detection, road feature detection and classification, wait condition identification and classification, etc. As a result, the HD map data may be leveraged during training such that the DNNs—in deployment—may aid autonomous machines in navigating environments safely without relying on HD map data to do so.

Abstract: In various examples, a machine learning model—such as a deep neural network (DNN)—may be trained to use image data and/or other sensor data as inputs to generate two-dimensional or three-dimensional trajectory points in world space, a vehicle orientation, and/or a vehicle state. For example, sensor data that represents orientation, steering information, and/or speed of a vehicle may be collected and used to automatically generate a trajectory for use as ground truth data for training the DNN. Once deployed, the trajectory points, the vehicle orientation, and/or the vehicle state may be used by a control component (e.g., a vehicle controller) for controlling the vehicle through a physical environment. For example, the control component may use these outputs of the DNN to determine a control profile (e.g., steering, decelerating, and/or accelerating) specific to the vehicle for controlling the vehicle through the physical environment.

Abstract: A power supply system suitable for use by an aircraft is disclosed. The power system converts power from an unregulated DC power source to multiple AC and DC voltage outputs. The power supply system comprises an interleaved buck converter, and interleaved full-bridge converter, an interleaved inverter, and a control system. In one configuration, the interleaved inverter uses high-voltage DC generated by the interleaved four-bridge converter as its power input to generate a high-voltage AC output.

Abstract: A wireless charger for an electric vehicle and a resonant inverter comprising a resonant portion that serially connects to a phase shifting portion and serially connects with a load component and a method for controlling a resonant inverter having multiple phase shifts, comprising operating the frequency of the resonant inverter close to the resonant frequency of the inverter through the full operation range of the resonant inverter; and adjusting phase shifts to control the output power of the resonant inverter.

Abstract: A system for wirelessly charging an electric vehicle comprises a wireless charging coil having a first end, a second end, and a plurality of turns. The first end has a first turn having a first radius r1 and the second end has an Nth turn having an Nth radius rN, rN<r1 such that the wireless charging coil is funnel shaped, where, an ascending distance between each adjacent turn of the wireless charging coil is: ??j,j+1=?j+i?3,j+i?2+(??34??i,i+1), for j?4 and r?j+1>0 where, ??j,j+1=r?j?r?j+1 is the distance between adjacent turns, ? is between 0.05% and 0.2%, i is 2 to N; j is 1 to N; and ??34 is an ascending distance between the third turn and the fourth turn.

Abstract: A power supply system is disclosed that includes a first interleaved power supply, a second interleaved power supply, and a common electromagnetic interference filter. The common electromagnetic interference filter is configured to provide DC power from a DC power source to both the first interleaved power supply and the second interleaved power supply. In one example, the common electromagnetic interference filter comprises a localized filter stage configured to receive DC power from the DC power source, and a distributed filter stage configured to receive DC power from the localized filter stage. The distributed filter stage includes a first set of common mode capacitors electrically connected to and physically proximate input power lines of the first interleaved power supply, and a second set of common mode capacitors electrically connected to and physically proximate input power lines of the second interleaved power supply.

Abstract: A power supply system suitable for use by an aircraft is disclosed. The power system converts power from an unregulated DC power source to multiple AC and DC voltage outputs. The power supply system comprises an interleaved buck converter, and interleaved full-bridge converter, an interleaved inverter, and a control system. In one configuration, the interleaved inverter uses high-voltage DC generated by the interleaved four-bridge converter as its power input to generate a high-voltage AC output.

Abstract: A wireless charger for an electric vehicle and a resonant inverter comprising a resonant portion that serially connects to a phase shifting portion and serially connects with a load component and a method for controlling a resonant inverter having multiple phase shifts, comprising operating the frequency of the resonant inverter close to the resonant frequency of the inverter through the full operation range of the resonant inverter; and adjusting phase shifts to control the output power of the resonant inverter.

Abstract: A power supply system suitable for use by an aircraft is disclosed. The power system converts power from an unregulated DC power source to multiple AC and DC voltage outputs. The power supply system comprises an interleaved buck converter, and interleaved full-bridge converter, an interleaved inverter, and a control system. In one configuration, the interleaved inverter uses high-voltage DC generated by the interleaved four-bridge converter as its power input to generate a high-voltage AC output.

Abstract: A system for wirelessly charging an electric vehicle comprises a wireless charging coil having a first end, a second end, and a plurality of turns. The first end has a first turn having a first radius r1 and the second end has an Nth turn having an Nth radius rN, rN<r1 such that the wireless charging coil is funnel shaped, where, an ascending distance between each adjacent turn of the wireless charging coil is: ?j,j+1?=?j+i?3,j+i?2+(?34???i,i+1), for j?4 and rj+1?>0 where, ?j,j+1?=rj??rj+1? is the distance between adjacent turns, ? is between 0.05% and 0.2%, i is 2 to N; j is 1 to N; and ?34? is an ascending distance between the third turn and the fourth turn.

Abstract: A power supply system is disclosed that includes a first interleaved power supply, a second interleaved power supply, and a common electromagnetic interference filter. The common electromagnetic interference filter is configured to provide DC power from a DC power source to both the first interleaved power supply and the second interleaved power supply. In one example, the common electromagnetic interference filter comprises a localized filter stage configured to receive DC power from the DC power source, and a distributed filter stage configured to receive DC power from the localized filter stage. The distributed filter stage includes a first set of common mode capacitors electrically connected to and physically proximate input power lines of the first interleaved power supply, and a second set of common mode capacitors electrically connected to and physically proximate input power lines of the second interleaved power supply.

Abstract: A power supply system suitable for use by an aircraft is disclosed. The power system converts power from an unregulated DC power source to multiple AC and DC voltage outputs. The power supply system comprises an interleaved buck converter, and interleaved full-bridge converter, an interleaved inverter, and a control system. In one configuration, the interleaved inverter uses high-voltage DC generated by the interleaved four-bridge converter as its power input to generate a high-voltage AC output.