Abstract: A method comprises: activating a full lane centering control mode in an advanced driver assistance system (ADAS) of a vehicle, wherein in the full lane centering control mode the ADAS keeps the vehicle centered on a trajectory of a lane regardless of surroundings of the vehicle and applies a first torque level against steering input that a driver applies using a steering wheel; detecting, in the full lane centering control mode, a driver input corresponding to a departure from the trajectory; and in response to the driver input, instead activating a manual lane biasing mode in which the ADAS applies a second torque level against the steering input, the second torque level lower than the first torque level, the manual lane biasing mode allowing the driver to steer away from the trajectory.

Abstract: A computer-implemented method comprising: receiving sensor data from sensors of a first vehicle associated with a first person, the sensor data generated by the sensors; applying a machine-learning algorithm to the sensor data to define a driving envelope for an assisted-driving system, the assisted-driving system installed in a second vehicle; and providing the driving envelope to the assisted-driving system installed in the second vehicle.

Abstract: A computer-implemented method comprises: receiving first telemetry data generated by sensors of respective first vehicles in a fleet; clustering the first telemetry data into groups, each of the groups representing a profile of one or more first drivers of the first vehicles in the fleet; receiving second telemetry data generated by sensors of a second vehicle controlled by a second driver; associating the second driver with a first group of the groups by classifying the received second telemetry data; providing a subset of the first telemetry data corresponding to the first cluster as a baseline dataset for training of machine learning algorithms; generating baseline tuning parameter values using the trained machine learning algorithms; and providing the baseline tuning parameter values to a driver assistance system of a third vehicle controlled by the second driver.

Abstract: A computer-implemented method comprising: receiving sensor data from sensors of a first vehicle associated with a first person, the sensor data generated by the sensors; applying a machine-learning algorithm to the sensor data to define a driving envelope for an assisted-driving system, the assisted-driving system installed in a second vehicle; and providing the driving envelope to the assisted-driving system installed in the second vehicle.

Abstract: A method to estimate range to a moving rigid body from a moving platform using a monocular camera. The method does not require the camera platform to maneuver in order to estimate range. The method relies on identification and tracking of certain principal features of the object. The method extracts a silhouette of an object from an obtained image and identifies two principal linear components of the silhouette. A normalized distance between the point of intersection of the two linear components and a centroid of the silhouette is computed, compared to a data set and used to determine a direction of movement of the object.

Abstract: A method to estimate range to a moving rigid body from a moving platform using a monocular camera. The method does not require the camera platform to maneuver in order to estimate range. The method relies on identification and tracking of certain principal features of the object. The method extracts a silhouette of an object from an obtained image and identifies two principal linear components of the silhouette. A normalized distance between the point of intersection of the two linear components and a centroid of the silhouette is computed, compared to a data set and used to determine a direction of movement of the object.

Abstract: A computerized aircraft system, such as an unmanned aircraft system (UAS) is provided with a cloud detection system. The UAS includes a monocular electro-optic or infra-red camera which acquires consecutively, in real time, a plurality of images within a field of view of the camera. The system identifies feature points in each of the consecutive images, and generates macro representations of cloud formations (3D representations of the clouds) based on tracking of the feature points across the plurality of images. A cloud avoidance system takes in nominal own-ship waypoints, compares those waypoints to the 3D cloud representations and outputs modified waypoints to avoid the detected clouds.