Abstract: A method is disclosed for detecting and classifying one or more traffic lights. The method may include converting an RGB frame to an HSV frame. The HSV frame may be filtered by at least one threshold value to obtain at least one saturation frame. At least one contour may be extracted from the at least one saturation frame. Accordingly, a first portion of the RGB may be cropped in order to encompass an area including the at least one contour. The first portion may then be classified by an artificial neural network to determined whether the first portion corresponds to a not-a-traffic-light class, a red-traffic-light class, a green-traffic-light class, a yellow-traffic-light class, or the like.

Abstract: The disclosure relates to systems and methods for estimating or determining the motion of a vehicle and/or the distance to objects within view of a rear camera. A method for rear obstacle detection using structure from motion includes identifying image features in a first frame corresponding to features in a second frame, wherein the first frame and the second frame comprise adjacent image frames captured by a rear-facing camera of a vehicle. The method includes determining parameters for a non-planar motion model based on the image features. The method includes determining camera motion based on the parameters for the non-planar motion model.

Abstract: The disclosure relates to systems and methods for estimating or determining the motion of a vehicle and/or the distance to objects within view of a camera. A system for determining the motion of a vehicle includes a monocular camera mounted on a vehicle, an image component, a feature component, a model parameter component, a model selection component, and a motion component. The image component obtains a series of image frames captured by the monocular camera. The feature component identifies corresponding image features in adjacent image frames within the series of image frames. The model parameter component determines parameters for a planar motion model and a non-planar motion model based on the image features. The model selection component selects the planar motion model or the non-planer motion model as a selected motion model. The motion component determines camera motion based on parameters for the selected motion model.

Abstract: A system for providing media content in a vehicle includes a vehicle information component, a scene status component, and a content component. The vehicle information component is configured to generate vehicle information about an environment or status of the vehicle based on sensor data from the one or more vehicle sensors. The scene status component is configured to provide the vehicle information to a content provider. The content component is configured to receive content from the content provider, wherein the content is configured for rendering by the media system of the vehicle based on the vehicle information. The content provider also provides the content to the media system of the vehicle for rendering.

Abstract: A system for providing media content in a vehicle includes a content component, a noise component, and a closed captioning component. The content component is configured to receive content from a content provider, wherein the content is configured for rendering by the media system of a vehicle. The noise component is configured to determine a noise level within a cabin of the vehicle. The closed captioning component is configured to, in response to determining that the noise level exceeds a threshold, trigger display of closed captioning for the content.