Abstract: An environment interactive system includes: a first camera capturing first images of an area external to the vehicle; a second camera capturing second images of an area interior to the vehicle; a telematics control module determining a geographical location of the vehicle; a viewing device displaying items adjacent to, on or in front of detected objects and in a FOV of an occupant. The items correspond to the detected objects; and an infotainment module. The infotainment module: detects the objects based on the first images and the location of the vehicle; based on the second images, tracks at least one of a location of an eye of the occupant or a location of a body part of the occupant other than the eye; and based on the location of the eye and the location of the body part, displays the items via the viewing device in the FOV.

Abstract: A gaming system of a vehicle includes: a game application embodying an interactive game and stored in memory; a sensor of a vehicle configured to determine a present condition while the vehicle is moving; a gaming module of the vehicle, the gaming module configured to, while the vehicle is moving: execute the game application; display a virtual environment of the interactive game via one or more displays in the vehicle; output sound of the interactive game via one or more speakers in the vehicle; control action within the virtual environment of the interactive game based on user input received via one or more input devices of the vehicle; and adjust one or more characteristics of the virtual environment of the interactive game based on the present condition.

Abstract: A system or method implemented by an autonomous vehicle involves determining a path plan to reach a destination from an origin. The path plan includes two or more path steps indicating tasks to be completed to reach the destination. The method includes, during traversal of the path plan by the autonomous vehicle, evaluating one or more of the two or more path steps of a planning horizon to determine a behavior plan for the planning horizon. The planning horizon is based on a current position of the autonomous vehicle, the behavior plan includes a speed and a trajectory, and the evaluating includes performing a cost analysis using a parallelized tree-based decision scheme at each of two or more simulation intervals within the planning horizon. The evaluating and the determining the behavior plan is repeated at two or more positions of the autonomous vehicle from the origin to the destination.

Abstract: An entertainment system of a vehicle includes: a plurality of cameras configured to capture images around an exterior of the vehicle; one or more displays; and an entertainment module configured to: based on the images, generate panoramic video; and display the panoramic video within the vehicle via the one or more displays.

Abstract: An entertainment system of a vehicle includes: a plurality of cameras configured to capture images around an exterior of the vehicle; one or more displays; and an entertainment module configured to: based on the images, generate panoramic video; and display the panoramic video within the vehicle via the one or more displays.

Abstract: An environment interactive system includes: a first camera capturing first images of an area external to the vehicle; a second camera capturing second images of an area interior to the vehicle; a telematics control module determining a geographical location of the vehicle; a viewing device displaying items adjacent to, on or in front of detected objects and in a FOV of an occupant. The items correspond to the detected objects; and an infotainment module. The infotainment module: detects the objects based on the first images and the location of the vehicle; based on the second images, tracks at least one of a location of an eye of the occupant or a location of a body part of the occupant other than the eye; and based on the location of the eye and the location of the body part, displays the items via the viewing device in the FOV.

Abstract: A gaming system of a vehicle includes: a game application embodying an interactive game and stored in memory; a sensor of a vehicle configured to determine a present condition while the vehicle is moving; a gaming module of the vehicle, the gaming module configured to, while the vehicle is moving: execute the game application; display a virtual environment of the interactive game via one or more displays in the vehicle; output sound of the interactive game via one or more speakers in the vehicle; control action within the virtual environment of the interactive game based on user input received via one or more input devices of the vehicle; and adjust one or more characteristics of the virtual environment of the interactive game based on the present condition.

Abstract: A method comprises detecting identities of occupants of a vehicle using any of a camera system, an audio system, a biometric sensing system, and a mobile device detection system in the vehicle. The method comprises matching the detected identities to corresponding profiles of the occupants. The profiles include permissions set by the occupants for collecting data about preferences of the occupants. The method comprises perceiving, based on the permissions in the profiles, preferences of the occupants for inputs received by the occupants. The perceiving includes capturing responses of the occupants to the inputs using any of the camera system, a haptic sensing system, and an audio system in the vehicle, and wherein the perceiving further includes processing the responses using trained models. The method comprises adding the preferences to the profiles to provide content to the occupants based on the preferences stored in the profiles.

Abstract: Automated driving systems, control logic, and methods execute maneuver criticality analysis to provide intelligent vehicle operation in transient driving conditions. A method for controlling an automated driving operation includes a vehicle controller receiving path plan data with location, destination, and predicted path data for a vehicle. From the received path plan data, the controller predicts an upcoming maneuver for driving the vehicle between start and goal lane segments. The vehicle controller determines a predicted route with lane segments connecting the start and goal lane segments, and segment maneuvers for moving the vehicle between the start, goal, and route lane segments. A cost value is calculated for each segment maneuver; the controller determines if a cost values exceeds a corresponding criticality value. If so, the controller commands a resident vehicle subsystem to execute a control operation associated with taking the predicted route.

Abstract: A system or method implemented by an autonomous vehicle involves determining a path plan to reach a destination from an origin. The path plan includes two or more path steps indicating tasks to be completed to reach the destination. The method includes, during traversal of the path plan by the autonomous vehicle, evaluating one or more of the two or more path steps of a planning horizon to determine a behavior plan for the planning horizon. The planning horizon is based on a current position of the autonomous vehicle, the behavior plan includes a speed and a trajectory, and the evaluating includes performing a cost analysis using a parallelized tree-based decision scheme at each of two or more simulation intervals within the planning horizon. The evaluating and the determining the behavior plan is repeated at two or more positions of the autonomous vehicle from the origin to the destination.

Abstract: Systems and methods are provided for controlling a vehicle. In one embodiment, a method includes: determining, by a processor, that a lane change is desired; determining, by the processor, a lane change action based on a reinforcement learning method and a rule-based method, wherein each of the methods evaluates lane data, vehicle data, map data, and actor data; and controlling, by the processor, the vehicle to perform the lane change based on the lane action.

Abstract: A method of updating an identification algorithm of a vehicle includes sensing an image and drawing boundary boxes in the image. The algorithm attempts to identify an object-of-interest within each respective boundary box. The algorithm also attempts to identify a component of the object-of-interest within each respective boundary box, and if component is identified, calculates an excluded amount of a component boundary that is outside an object boundary. When the excluded amount is greater than a coverage threshold, the algorithm communicates the image to a processing center, which may identify a previously un-identified the object-of-interest in the image. The processing center may add the image to a training set of images to define a revised training set of images, and retrain the identification algorithm using the revised training set of images. The updated identification algorithm may then be uploaded onto the vehicle.

Abstract: Presented are automated driving systems and control logic for intelligent vehicle operation in transient driving conditions, methods for constructing/operating such systems, and vehicles equipped with such systems. A method for controlling an automated driving operation includes a vehicle controller receiving path plan data with location, destination, and predicted path data for a vehicle. From the received path plan data, the controller predicts an upcoming maneuver for driving the vehicle between start and goal lane segments. The vehicle controller determines a predicted route with lane segments connecting the start and goal lane segments, and segment maneuvers for moving the vehicle between the start, goal, and route lane segments. A cost value is calculated for each segment maneuver; the controller determines if a cost values exceeds a corresponding criticality value.

Abstract: A method of updating an identification algorithm of a vehicle includes sensing an image and drawing boundary boxes in the image. The algorithm attempts to identify an object-of-interest within each respective boundary box. The algorithm also attempts to identify a component of the object-of-interest within each respective boundary box, and if component is identified, calculates an excluded amount of a component boundary that is outside an object boundary. When the excluded amount is greater than a coverage threshold, the algorithm communicates the image to a processing center, which may identify a previously un-identified the object-of-interest in the image. The processing center may add the image to a training set of images to define a revised training set of images, and retrain the identification algorithm using the revised training set of images. The updated identification algorithm may then be uploaded onto the vehicle.

Abstract: Technical solutions are described for maneuvering a vehicle autonomously is described. An example method includes receiving, by a controller, a command to navigate the vehicle, the command based on a global route calculation. The method further includes ranking, by the controller, maneuver patterns from a list of maneuver patterns to generate a ranked list of maneuver patterns. The method further includes selecting a maneuver pattern from the ranked list, and in response to a comfort score assigned to the selected maneuver pattern being above a predetermined threshold, executing the maneuver pattern.

Abstract: A method and apparatus for detecting motion of a slow-moving vehicle are provided. The method includes detecting a wheel of the vehicle in a plurality of frames of a video, generating bounding boxes around portions of the frames including the wheel of the vehicle, scaling the portions of the frames including the wheel of the vehicle to a predetermined constant size, determining whether the wheel of the vehicle is moving by analyzing the scaled portions of the image, and outputting information indicating that the vehicle is moving if the determining determines that the wheel of the vehicle is moving.

Abstract: A method for controlling an operating vehicle includes: (a) determining, via a controller, a confidence level that the light of the other vehicle is ON based on images captured by a camera of the operating vehicle; and (b) controlling, via the controller, an alarm of the operating vehicle based on the confidence level that the light of the other vehicle is ON.

Abstract: A method and system of diagnosing a lubrication system of an engine includes determining a lubrication system fault and controlling an engine in response to the fault. The method is operative to first determine a poor state of health for a lubrication system, then determine an oil degradation or lube system fault. In response to a lube system fault, engine operation is altered in order to reduce the negative effects of the lube system fault such as increasing minimum idle speed in response to reduced oil pressure.

Abstract: A method for controlling an operating vehicle includes: (a) determining, via a controller, a confidence level that the light of the other vehicle is ON based on images captured by a camera of the operating vehicle; and (b) controlling, via the controller, an alarm of the operating vehicle based on the confidence level that the light of the other vehicle is ON.

Abstract: Systems and methods are provided for controlling a vehicle. In one embodiment, a method includes receiving vehicle state data, high-definition map data, and vehicle object environment data, generating a trajectory path that is optimal with respect to the received data, determining whether to update the trajectory path using Bézier curves based on the received data, performing an assessment of the trajectory using properties of Bézier curves, and generating an updated trajectory.