Abstract: A computer that includes a processor and a memory, the memory including instructions executable by the processor to input real world coordinates of a real world location and a real world time, number and orbit data of GNSS satellites, terrain model data, and atmospheric model data to a first machine learning model to generate a simulated GNSS location and heading based on the real world location and a simulated GNSS error in location and heading in the real world coordinates. The first machine learning model can be trained based on an acquired GNSS location, an acquired GNSS time, an acquired number and orbit data of the GNSS satellites, an acquired GNSS DOP from real world systems, the terrain model data, and the atmospheric model data. Simulated GNSS data including the simulated GNSS location and heading can be generated to perform one or more of training and testing of a simulated vehicle operation system.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to generate a corridor defining a geometric area surrounding a road-level navigation route for a vehicle; apply the corridor to a lane-level map, the lane-level map including a plurality of lane segments, each lane segment defining a lane of a road from one endpoint to another endpoint; populate a lane-segment set with a subset of the lane segments from the lane-level map based on the application of the corridor to the lane-level map; and determine a lane-level navigation route based on the lane-segment set, the lane-level navigation route following the road-level navigation route.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive a positional deviation for a vehicle and update a classification of a geographic area in a GNSS deviation map layer based on the positional deviation. The positional deviation is based on sensor data generated by environmental sensors on board the vehicle, map data, and global navigation satellite system (GNSS) data received at the vehicle. The positional deviation indicates a difference between a GNSS pose of the vehicle derived from the GNSS data and a localized position of the vehicle indicated by the sensor data and the map data. The GNSS deviation map layer indicates a reliability of the GNSS data.

Abstract: A computer that includes a processor and a memory, the memory including instructions executable by the processor to receive Global Navigation Satellite System (GNSS) error predictions corresponding to respective potential paths between a vehicle location and a specified destination. The memory includes instructions to identify a lowest-cost path from the potential paths based on path costs determined based on the respective GNSS error predictions for the potential paths. A propulsion subsystem and a steering subsystem of the vehicle are controlled to operate the vehicle along the lowest-cost path.

Abstract: A bus bar current interruption device comprising a casing, providing an enclosure, and a bus bar arrangement. The bus bar arrangement is arranged to extend through the enclosure and comprises a first terminal end portion and a second terminal end portion, each of said first and second terminal end portions arranged outside of the casing. The bus bar arrangement further comprised a first fuse portion and a second fuse portion arranged in the enclosure. The casing comprises an exhaust opening configured to allow escape of vapor from the enclosure. The exhaust opening is fluidly connected to an exhaust chamber or conduit formed by the casing or attached to the casing.

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for training data collection and evaluation for automatic SOAP note generation. Training data is accessed, and evaluation process is performed on the training data to result in evaluated training data. A fine-tuned machine-learning model is generated using the evaluated training data. The fine-tuned machine-learning model can be used to perform a task associated with generating a SOAP note.

Abstract: The present disclosure relates generally to techniques for analyzing and improving a bot system, and more particularly to an analytic system integrated with a bot system for monitoring, analyzing, visualizing, diagnosing, and improving the performance of the bot system. For example, an analytic system is integrated with a bot system for monitoring, analyzing, visualizing, and improving the performance of the bot system. The analytic system monitors events occurred in conversations between end users and the bot system, aggregates and analyzes the collected events, and provides information regarding the conversations graphically on a graphic user interface as insights reports at different generalization levels. The insights reports offer developer-oriented analytics to pinpoint issues with skills so a user can address them before they cause problems.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to determine an estimated pose of a vehicle in a global reference frame having a first error and to determine an estimated second error based on a combination of the first error and a map error, in which the map error represents a difference between the estimated pose of the vehicle in the global reference frame and a corresponding estimated pose in a map-referenced frame. The stored instructions being additionally to predict a third error in a future map-referenced measurement frame based on the estimated second error and a motion model and to compute an update to the third error by combining the predicted third error with an accumulation of instantaneous vehicle position and heading errors obtained via a comparison between a camera-observed feature and a corresponding feature from a digital map.

Abstract: An example system can include: at least one processor; and non-transitory computer-readable storage storing instructions that, when executed by the at least one processor, cause the system to: generate an ingestion manager programmed to ingest data associated with a job; and generate a logging manager programmed to capture metadata associated with the job; wherein the ingestion manager is programmed to automatically retry the job based upon the metadata captured by the logging manager.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to determine an estimated pose of a vehicle in a global reference frame having a first error and to determine an estimated second error based on a combination of the first error and a map error, in which the map error represents a difference between the estimated pose of the vehicle in the global reference frame and a corresponding estimated pose in a map-referenced frame. The stored instructions being additionally to predict a third error in a future map-referenced measurement frame based on the estimated second error and a motion model and to compute an update to the third error by combining the predicted third error with an accumulation of instantaneous vehicle position and heading errors obtained via a comparison between a camera-observed feature and a corresponding feature from a digital map.

Abstract: Systems and methods for transforming autonomous aerial vehicle sensor data between platforms are disclosed herein. An example method can include receiving, by an UAV, vehicle radar data and radar calibration data from a vehicle, as well as location information for a location of the vehicle, determining a simulated UAV at the location, establishing an orientation for a simulated radar on a bottom of the simulated UAV, determining a height for the simulated UAV to match a field of view of the simulated radar, performing a geometrical transformation to convert the vehicle radar data set into a UAV perspective, converting the vehicle radar data into a vehicle coordinate frame using the radar calibration using vehicle global positioning system (GPS) coordinates, converting the vehicle coordinate frame from a global frame into a UAV coordinate frame using UAV GPS coordinates, and converting the UAV coordinate frame into simulated radar sensor frame.

Abstract: A fastening clip (100), for securing a first component (200) and a second component (300), includes a body portion (102) and a collar (104) formed at an edge of the body portion (102). The body portion (102) includes a flat surface (106) having a first face and a second face, a locking element (110) extending from the first face of the flat surface (106) and adapted to detachably couple to the first component (200), and a plurality of engagement portions (108) extending from the first face of the flat surface (106) and adapted to detachably couple to the second component (300). The collar (104) includes a first flange (112) extending from the second face in an opposite direction from the plurality of engagement portions. The first flange (112) is flexible and is adapted to abut against the first component (200).

Abstract: Examples of rivet-type fastener are described. The rivet-type fastener may further include a fastener pin. The fastener pin includes a planar head portion and a shaft longitudinally extending from the head portion. The fastener pin may further include a cantilevered snap element at a distal end of the shaft. A portion of the cantilevered snap element is flexibly moveable with respect to the axis of shaft. The rivet-type fastener comprises a fastener body which in turn comprises a collared portion with a central opening. A plurality of elongated portions extend orthogonally from the collared portion, wherein the elongated portions are radially positioned about the central axis and about the edges of the central opening.

Abstract: Systems and methods for providing agricultural operation(s) for a crop, in manner consistent with a predicted quality of the crop are described. One example computer-implemented method includes accessing, by a computing device, data associated with a crop in a target field, where the data includes planting data for the crop and weather data for the target field, and correlating the weather data to each of a plurality of growth stages for the crop in the target field. The method then includes, prior to a harvest of the crop from the target field, determining, by the computing device, a quality of the crop in the target field, based on the correlated weather data and a quality model specific to a type of the crop, and directing at least one agricultural operation consistent with the determined quality of the crop in the target field.

Abstract: Systems and methods for transforming autonomous aerial vehicle sensor data between platforms are disclosed herein. An example method can include receiving, by an UAV, vehicle radar data and radar calibration data from a vehicle, as well as location information for a location of the vehicle, determining a simulated UAV at the location, establishing an orientation for a simulated radar on a bottom of the simulated UAV, determining a height for the simulated UAV to match a field of view of the simulated radar, performing a geometrical transformation to convert the vehicle radar data set into a UAV perspective, converting the vehicle radar data into a vehicle coordinate frame using the radar calibration using vehicle global positioning system (GPS) coordinates, converting the vehicle coordinate frame from a global frame into a UAV coordinate frame using UAV GPS coordinates, and converting the UAV coordinate frame into simulated radar sensor frame.

Abstract: The present disclosure relates generally to techniques for analyzing and improving a bot system, and more particularly to an analytic system integrated with a bot system for monitoring, analyzing, visualizing, diagnosing, and improving the performance of the bot system. For example, an analytic system is integrated with a bot system for monitoring, analyzing, visualizing, and improving the performance of the bot system. The analytic system monitors events occurred in conversations between end users and the bot system, aggregates and analyzes the collected events, and provides information regarding the conversations graphically on a graphic user interface as insights reports at different generalization levels. The insights reports offer developer-oriented analytics to pinpoint issues with skills so a user can address them before they cause problems.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to input a fisheye image to a vector quantized variational autoencoder. The vector quantized variational autoencoder can encode the fisheye image to first latent variables based on an encoder. The vector quantized variational autoencoder can quantize the first latent variables to generate second latent variables based on a dictionary of embeddings. The vector quantized variational autoencoder can decode the second latent variables to a rectified rectilinear image using a decoder and output the rectified rectilinear image.

Abstract: The present disclosure relates generally to techniques for analyzing and improving a bot system, and more particularly to an analytic system integrated with a bot system for monitoring, analyzing, visualizing, diagnosing, and improving the performance of the bot system. For example, an analytic system is integrated with a bot system for monitoring, analyzing, visualizing, and improving the performance of the bot system. The analytic system monitors events occurred in conversations between end users and the bot system, aggregates and analyzes the collected events, and provides information regarding the conversations graphically on a graphic user interface as insights reports at different generalization levels. The insights reports offer developer-oriented analytics to pinpoint issues with skills so a user can address them before they cause problems.

Abstract: A fastening clip (100), for securing a first component (200) and a second component (300), includes a body portion (102) and a collar (104) formed at an edge of the body portion (102). The body portion (102) includes a flat surface (106) having a first face and a second face, a locking element (110) extending from the first face of the flat surface (106) and adapted to detachably couple to the first component (200), and a plurality of engagement portions (108) extending from the first face of the flat surface (106) and adapted to detachably couple to the second component (300). The collar (104) includes a first flange (112) extending from the second face in an opposite direction from the plurality of engagement portions. The first flange (112) is flexible and is adapted to abut against the first component (200).

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to input a fisheye image to a vector quantized variational autoencoder. The vector quantized variational autoencoder can encode the fisheye image to first latent variables based on an encoder. The vector quantized variational autoencoder can quantize the first latent variables to generate second latent variables based on a dictionary of embeddings. The vector quantized variational autoencoder can decode the second latent variables to a rectified rectilinear image using a decoder and output the rectified rectilinear image.