Abstract: Methods and apparatus for illuminating a biometric aspect, such as periocular region features or iris features, with multiple illumination configurations to create multiple illumination channels for computer analysis are disclosed. Multiple illumination channels may be used for quality assurance by comparing the actual illumination channel property of a given image to the illumination channel that was activated for image capture. Images captured at the multiple illumination channels can also be used in biometric authentication by providing additional information for comparison to a stored template generated from images taken at multiple illumination channels during enrollment. Biometric authentication using a quality assurance process may be resistant to attempts to bypass biometric authentication.

Abstract: Methods for performing user authentication based on a multi-representation eye model for devices such as head-mounted display devices are disclosed. The multi-representation eye model may be generated and updated based on feature representations of an identified eye. The user authentication process may be initiated by causing an image to be captured of a current eye under a first set of conditions that may then be transformed into a current feature representation. The current feature representation may be applied to the multi-representation eye model to determine whether the current eye is a match for the identified eye.

Abstract: A computer-implemented method of determining relative velocity between a vehicle and an object. The method includes receiving sensor data generated by one or more sensors of the vehicle configured to sense an environment by following a scan pattern comprising component scan lines. The method includes obtaining, based on the sensor data, a point cloud frame. Additionally, the method includes identifying a first pixel and a second pixel that are co-located within a field of regard and overlap a point cloud object within the point cloud frame and calculating a difference between a depth associated with the first pixel and a depth associated with the second pixel. The method includes determining a relative velocity of the point cloud object by dividing the difference in depth data by a time difference between when the depth associated with the first pixel was sensed and the depth associated with the second pixel was sensed.

Abstract: Various software techniques for managing operation of autonomous vehicles based on sensor data are disclosed herein. A computing system may generate, based on a set of signals descriptive of a current state of an environment in which the autonomous vehicle is operating, a normal path plan separate from a safe path plan, or a hybrid path plan including a normal path plan and a safe path plan. In generating the safe path plan, the computing system may generate and concatenate a set of motion primitives. When a fault condition occurs, the computing device may transition from executing the normal path plan to executing the safe path plan to safely stop the autonomous vehicle.

Abstract: A computer-implemented method of determining a relative velocity between a vehicle and an object. The method includes receiving sensor data generated by one or more sensors of the vehicle configured to sense an environment following a scan pattern. The method also includes obtaining, based on the sensor data, a point cloud frame. The point cloud frame comprises a plurality of points of depth data and a time at which the depth data was captured. Additionally, the method includes selecting two or more points of the scan pattern that overlap the object. The selected points are located on or near a two-dimensional surface corresponding to the object, and the depth data for two or more of the selected points are captured at different times. The method includes calculating the relative velocity between the vehicle and the object based on the depth data and capture times associated with the selected points.

Abstract: A computer-implemented method of determining relative velocity between a vehicle and an object. The method includes receiving sensor data generated by one or more sensors of the vehicle configured to sense an environment by following a scan pattern comprising component scan lines. The method includes obtaining, based on the sensor data, a point cloud frame. Additionally, the method includes identifying a first pixel and a second pixel that are co-located within a field of regard and overlap a point cloud object within the point cloud frame and calculating a difference between a depth associated with the first pixel and a depth associated with the second pixel. The method includes determining a relative velocity of the point cloud object by dividing the difference in depth data by a time difference between when the depth associated with the first pixel was sensed and the depth associated with the second pixel was sensed.

Abstract: A computer-implemented method of determining a relative velocity between a vehicle and an object. The method includes receiving sensor data generated by one or more sensors of the vehicle configured to sense an environment following a scan pattern. The method also includes obtaining, based on the sensor data, a point cloud frame. The point cloud frame comprises a plurality of points of depth data and a time at which the depth data was captured. Additionally, the method includes selecting two or more points of the scan pattern that overlap the object. The selected points are located on or near a two-dimensional surface corresponding to the object, and the depth data for two or more of the selected points are captured at different times. The method includes calculating the relative velocity between the vehicle and the object based on the depth data and capture times associated with the selected points.

Abstract: Various software techniques for managing operation of autonomous vehicles based on sensor data are disclosed herein. A computing system may generate, based on a set of signals descriptive of a current state of an environment in which the autonomous vehicle is operating, a normal path plan separate from a safe path plan, or a hybrid path plan including a normal path plan and a safe path plan. In generating the safe path plan, the computing system may generate and concatenate a set of motion primitives. When a fault condition occurs, the computing device may transition from executing the normal path plan to executing the safe path plan to safely stop the autonomous vehicle.

Abstract: Various software techniques for managing operation of autonomous vehicles based on sensor data are disclosed herein. A computing system may generate, based on a set of signals descriptive of a current state of an environment in which the autonomous vehicle is operating, a normal path plan separate from a safe path plan, or a hybrid path plan including a normal path plan and a safe path plan. In generating the safe path plan, the computing system may generate and concatenate a set of motion primitives. When a fault condition occurs, the computing device may transition from executing the normal path plan to executing the safe path plan to safely stop the autonomous vehicle.