Abstract: Example systems and methods are disclosed for determining vehicle pose data for an autonomous vehicle. The vehicle computer system may receive pose data from multiple pose measurement systems of the autonomous vehicle. Each pose measurement system may include one or more corresponding sensors of the autonomous vehicle. The vehicle computer system may determine a pose data quality for the received pose data for each pose measurement system. The vehicle computer system may set the vehicle pose data to the pose data of the pose measurement system with the highest pose data quality. The vehicle computer system may control the autonomous vehicle based on the vehicle pose data.

Abstract: The present disclosure relates to methods and systems that facilitate determination of a pose of a vehicle based on various combinations of map data and sensor data received from light detection and ranging (LIDAR) devices and/or camera devices. An example method includes receiving point cloud data from a (LIDAR) device and transforming the point cloud data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error. An alternative method includes receiving camera image data from a camera and transforming the camera image data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error.

Abstract: The present disclosure relates to methods and systems that facilitate determination of a pose of a vehicle based on various combinations of map data and sensor data received from light detection and ranging (LIDAR) devices and/or camera devices. An example method includes receiving point cloud data from a (LIDAR) device and transforming the point cloud data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error. An alternative method includes receiving camera image data from a camera and transforming the camera image data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error.

Abstract: In one example, a vehicle includes a platform and a yaw sensor mounted on the platform. The yaw sensor provides an indication of a yaw rate of rotation of the yaw sensor. The vehicle also includes an actuator that rotates the platform. The vehicle also includes a controller coupled to the yaw sensor and the actuator. The controller receives the indication of the yaw rate from the yaw sensor. The controller also causes the actuator to rotate the platform (i) along a direction of rotation opposite to a direction of the rotation of the yaw sensor and (ii) at a rate of rotation based on the yaw rate of the yaw sensor. The controller also estimates a direction of motion of the vehicle in an environment of the vehicle based on at least the rate of rotation of the platform.

Abstract: The present disclosure relates to methods and systems that facilitate determination of a pose of a vehicle based on various combinations of map data and sensor data received from light detection and ranging (LIDAR) devices and/or camera devices. An example method includes receiving point cloud data from a (LIDAR) device and transforming the point cloud data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error. An alternative method includes receiving camera image data from a camera and transforming the camera image data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error.

Abstract: The present disclosure relates to methods and systems that facilitate determination of a pose of a vehicle based on various combinations of map data and sensor data received from light detection and ranging (LIDAR) devices and/or camera devices. An example method includes receiving point cloud data from a (LIDAR) device and transforming the point cloud data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error. An alternative method includes receiving camera image data from a camera and transforming the camera image data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error.

Abstract: Example systems and methods are disclosed for determining vehicle pose data for an autonomous vehicle. The vehicle computer system may receive pose data from multiple pose measurement systems of the autonomous vehicle. Each pose measurement system may include one or more corresponding sensors of the autonomous vehicle. The vehicle computer system may determine a pose data quality for the received pose data for each pose measurement system. The vehicle computer system may set the vehicle pose data to the pose data of the pose measurement system with the highest pose data quality. The vehicle computer system may control the autonomous vehicle based on the vehicle pose data.

Abstract: In one example, a vehicle includes a platform and a yaw sensor mounted on the platform. The yaw sensor provides an indication of a yaw rate of rotation of the yaw sensor. The vehicle also includes an actuator that rotates the platform. The vehicle also includes a controller coupled to the yaw sensor and the actuator. The controller receives the indication of the yaw rate from the yaw sensor. The controller also causes the actuator to rotate the platform (i) along a direction of rotation opposite to a direction of the rotation of the yaw sensor and (ii) at a rate of rotation based on the yaw rate of the yaw sensor. The controller also estimates a direction of motion of the vehicle in an environment of the vehicle based on at least the rate of rotation of the platform.

Abstract: In one example, a vehicle includes a platform and a yaw sensor mounted on the platform. The yaw sensor provides an indication of a yaw rate of rotation of the yaw sensor. The vehicle also includes an actuator that rotates the platform. The vehicle also includes a controller coupled to the yaw sensor and the actuator. The controller receives the indication of the yaw rate from the yaw sensor. The controller also causes the actuator to rotate the platform (i) along a direction of rotation opposite to a direction of the rotation of the yaw sensor and (ii) at a rate of rotation based on the yaw rate of the yaw sensor. The controller also estimates a direction of motion of the vehicle in an environment of the vehicle based on at least the rate of rotation of the platform.

Abstract: The present disclosure relates to methods and systems that facilitate determination of a pose of a vehicle based on various combinations of map data and sensor data received from light detection and ranging (LIDAR) devices and/or camera devices. An example method includes receiving point cloud data from a (LIDAR) device and transforming the point cloud data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error. An alternative method includes receiving camera image data from a camera and transforming the camera image data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error.

Abstract: Example systems and methods are disclosed for determining vehicle pose data for an autonomous vehicle. The vehicle computer system may receive pose data from multiple pose measurement systems of the autonomous vehicle. Each pose measurement system may include one or more corresponding sensors of the autonomous vehicle. The vehicle computer system may determine a pose data quality for the received pose data for each pose measurement system. The vehicle computer system may set the vehicle pose data to the pose data of the pose measurement system with the highest pose data quality. The vehicle computer system may control the autonomous vehicle based on the vehicle pose data.

Abstract: In one example, a vehicle includes a platform and a yaw sensor mounted on the platform. The yaw sensor provides an indication of a yaw rate of rotation of the yaw sensor. The vehicle also includes an actuator that rotates the platform. The vehicle also includes a controller coupled to the yaw sensor and the actuator. The controller receives the indication of the yaw rate from the yaw sensor. The controller also causes the actuator to rotate the platform (i) along a direction of rotation opposite to a direction of the rotation of the yaw sensor and (ii) at a rate of rotation based on the yaw rate of the yaw sensor. The controller also estimates a direction of motion of the vehicle in an environment of the vehicle based on at least the rate of rotation of the platform.

Abstract: Example systems and methods are disclosed for determining vehicle pose data for an autonomous vehicle. The vehicle computer system may receive pose data from multiple pose measurement systems of the autonomous vehicle. Each pose measurement system may include one or more corresponding sensors of the autonomous vehicle. The vehicle computer system may determine a pose data quality for the received pose data for each pose measurement system. The vehicle computer system may set the vehicle pose data to the pose data of the pose measurement system with the highest pose data quality. The vehicle computer system may control the autonomous vehicle based on the vehicle pose data.

Abstract: Systems and methods are provided that may optimize basic models of an intersection in a roadway with high intensity image data of the intersection of the roadway. More specifically, parameters that define the basic model of the intersection in the roadway may be adjusted to more accurately define the intersection. For example, by comparing a shape of the intersection predicted by the basic model with extracted curbs and lane boundaries from elevation and intensity maps, the intersection parameters can be optimized to match real intersection-features in the environment. Once the optimal intersection parameters have been found, roadgraph features describing the intersection may be extracted.

Abstract: Systems and methods are provided that may optimize basic models of an intersection in a roadway with high intensity image data of the intersection of the roadway. More specifically, parameters that define the basic model of the intersection in the roadway may be adjusted to more accurately define the intersection. For example, by comparing a shape of the intersection predicted by the basic model with extracted curbs and lane boundaries from elevation and intensity maps, the intersection parameters can be optimized to match real intersection-features in the environment. Once the optimal intersection parameters have been found, roadgraph features describing the intersection may be extracted.

Abstract: Methods and systems for correcting an estimated heading using a map are disclosed. Map data indicative of a map of an environment of a vehicle and data indicative of an estimated heading the vehicle is received. A sensor obtains first spatial data indicative of locations of objects in the environment relative to the vehicle at a first time. A first location of the vehicle on the map is determined based on the first spatial data. The sensor obtains second spatial data indicative of locations of objects in the environment relative to the vehicle at a second time. A second location of the vehicle on the map is determined based on the second spatial data. A heading correction of the vehicle is determined based on the estimated heading, the first location, the first time, the second location, and the second time, and a speed of the vehicle.

Abstract: The invention provides methods for treating muscle loss in an individual. In one embodiment, the invention includes administering to an individual an effective amount of a branched chain amino acid (BCAA), a BCAA precursor, a BCAA metabolite, a BCAA-rich protein, a protein manipulated to enrich the BCAA content or any combination thereof. The invention further provides nutritional products for such administration, including orally-administrable nutritional products.

Abstract: The invention characterizes and provides a receptor for Proteolysis Inducing Factor (PIF) and associated methods and materials employing the same. These have utility, for example, in the provision of treatments for cachexia.

Abstract: The invention characterises and provides a receptor for Proteolysis Inducing Factor (PIF) and associated methods and materials employing the same. These have utility, for example, in the provision of treatments for cachexia.

Abstract: The present invention relates to compositions and methods for preventing and treating a condition in a mammalian subject that include at least one inhibitor of double stranded RNA dependent protein kinase (PKR-I) prior to or concurrently with the treatment, wherein the treatment results to an inhibition of activation of dsRNA-dependent protein kinase. The compositions and methods of the present invention further include at least one potentiator that further enhances the inhibition of phosphorylation by PKR-I.