Abstract: Systems and methods for managing driver habits are disclosed herein. One embodiment learns undesirable driving habits of a driver over time as the driver operates a vehicle; identifies, for each learned undesirable driving habit, one or more situational triggers associated with that undesirable driving habit; receives information from one or more of vehicle sensors and one or more external sources; predicts that the driver will engage in a particular undesirable driving habit; and carries out one or more of the following avoidance strategies to assist the driver in refraining from engaging in the particular undesirable driving habit: communicating one or more speed advisories to the driver; suggesting an alternate route to the driver; and presenting the driver with one or more of a coupon, an offer, and a discount at a place of business to encourage the driver to take a break by stopping at the place of business.

Abstract: Methods and apparatus are provided for 3D object bounding for 2D image data for use in an assisted driving equipped vehicle. In various embodiments, an apparatus includes a camera operative to capture a two dimensional image of a field of view, a lidar operative to generate a point cloud of the field of view, a processor operative to generate a three dimensional representation of the field of view in response to the point cloud, to detect an object within the three dimensional representation, to generate a three dimensional bounding box in response to the object, to project the three dimensional bounding box onto the two dimensional image to generate a labeled two dimensional image, and a vehicle controller to controlling a vehicle in response to the labeled two dimensional image.

Abstract: Systems and method are provided for training an autonomous vehicle. In various embodiments, a method includes: storing, in a data storage device, real world data including a sequence of images of a road environment, the sequence of images generated based on a vehicle traversing the road environment; processing, in an offline simulation environment, the sequence of images with a deep reinforcement learning agent associated with a control feature of the autonomous vehicle to obtain an optimized set of control policies; and training the autonomous vehicle based on the optimized set of control polices.

Abstract: The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the method and apparatus are operative for determining a following distance between a host vehicle and a lead vehicle and a lead vehicle speed, generating a lane change request in response to the following distance, a host vehicle speed and the lead vehicle speed, determining an available lane in response to an image and the lane change request, generating a lane change command in response to the available lane, generating a control signal in response to the lane change request and a map data and controlling the vehicle to execute a lane change action in response to the control signal.

Abstract: A system for partitioning a two-dimensional (2D) array and recognizing elements of the 2D array selects a location in an array comprising pixel data of the 2D array; and selects a direction in which to traverse the array from the selected location to segment the elements of the 2D array. The system uses a model and a reward/penalty function to guide the traversal through the array. The system changes direction when the data encountered while traversing the array in the selected direction represents one of the elements of the 2D array and continues in the selected direction when the data represents a joint between two elements of the 2D array. The system recognizes one or more of the elements of the 2D array segmented by traversing the array.

Abstract: A system for partitioning a two-dimensional (2D) array and recognizing elements of the 2D array selects a location in an array comprising pixel data of the 2D array; and selects a direction in which to traverse the array from the selected location to segment the elements of the 2D array. The system uses a model and a reward/penalty function to guide the traversal through the array. The system changes direction when the data encountered while traversing the array in the selected direction represents one of the elements of the 2D array and continues in the selected direction when the data represents a joint between two elements of the 2D array. The system recognizes one or more of the elements of the 2D array segmented by traversing the array.