Abstract: The present disclosure relates to providing personalized service schedule in a computing network for a service provider to provide a service. In particular, the systems described herein utilize signal history of a plurality of users to train a model and to predict a cumulative signal amount for an individual user for a predetermined time frame in the future by drawing inferences from the model. The system described herein further transforms the predicted cumulative signal amounts to activity data and a personalized service schedule for the individual user may be updated based on the predicted activity data. The personalized service schedule may be utilized by disabling the service, or pausing the service or pausing a feature of the service when the predicted activity data indicates that the user is likely to be inactive.

Abstract: The present disclosure relates to providing personalized service schedule in a computing network for a service provider to provide a service. In particular, the systems described herein utilize signal history of a plurality of users to train a model and to predict a cumulative signal amount for an individual user for a predetermined time frame in the future by drawing inferences from the model. The system described herein further transforms the predicted cumulative signal amounts to activity data and a personalized service schedule for the individual user may be updated based on the predicted activity data. The personalized service schedule may be utilized by disabling the service, or pausing the service or pausing a feature of the service when the predicted activity data indicates that the user is likely to be inactive.

Abstract: The present disclosure relates to providing personalized service schedule in a computing network for a service provider to provide a service. In particular, the systems described herein utilize signal history of a plurality of users to train a model and to predict a cumulative signal amount for an individual user for a predetermined time frame in the future by drawing inferences from the model. The system described herein further transforms the predicted cumulative signal amounts to activity data and a personalized service schedule for the individual user may be updated based on the predicted activity data. The personalized service schedule may be utilized by disabling the service, or pausing the service or pausing a feature of the service when the predicted activity data indicates that the user is likely to be inactive.

Abstract: The present disclosure is directed toward a method and system for deconstructing and searching binary based vehicular data. The method includes extracting a vehicle filename metadata for one or more vehicular files; parsing data of the vehicular files to generate categorized objects based on a vehicular data dictionary for the vehicular files; associating each of the categorized objects with a respective vehicle filename metadata; storing vehicular documents indicative of the categorized objects with associated vehicle filename metadata in a database system; building a query based on a search input acquired by way of a search interface being displayed; and transmitting search results in response to the search input. The vehicular data dictionary defines multiple terms indicative of decoding logic provided in the vehicular files.

Abstract: A vehicle is described that includes a video camera configured to record video, a processor configured to process the recorded video from the video camera into hyperlapse video, and an input/output device to link the hyperlapse video with a social network associated with at least one of a driver of the vehicle and the vehicle. The processor may be configured to receive video data from the video camera and to create the hyperlapse video by reconstructing a video stream from the video data, planning a smooth path through the video stream, and rendering the video data along the smooth path into the hyperlapse video that has a faster speed and less jitter than the video stream. The hyperlapse video may be a non-native, sped-up video of the recorded video from the video camera.

Abstract: Techniques and examples pertaining to objection detection and trajectory prediction for autonomous vehicles are described. A processor receives an input stream of image frames and fuses a spatiotemporal input stream of the image frames and an appearance-based stream of the image frames using a deep neural network (DNN) to generate an augmented stream of the image frames. The processor performs object detection and trajectory prediction of one or more objects in the image frames based on the augmented stream.

Abstract: The present disclosure is directed toward a method and system for deconstructing and searching binary based vehicular data. The method includes extracting a vehicle filename metadata for one or more vehicular files; parsing data of the vehicular files to generate categorized objects based on a vehicular data dictionary for the vehicular files; associating each of the categorized objects with a respective vehicle filename metadata; storing vehicular documents indicative of the categorized objects with associated vehicle filename metadata in a database system; building a query based on a search input acquired by way of a search interface being displayed; and transmitting search results in response to the search input. The vehicular data dictionary defines multiple terms indicative of decoding logic provided in the vehicular files.

Abstract: Techniques and examples pertaining to objection detection and trajectory prediction for autonomous vehicles are described. A processor receives an input stream of image frames and fuses a spatiotemporal input stream of the image frames and an appearance-based stream of the image frames using a deep neural network (DNN) to generate an augmented stream of the image frames. The processor performs object detection and trajectory prediction of one or more objects in the image frames based on the augmented stream.

Abstract: A vehicle includes a steering wheel and a controller. The controller is configured to, in response to receiving location and speed data from other vehicles indicating an expected collision absent a trajectory change, automatically control the steering wheel to direct the vehicle along a collision avoidance path. The collision avoidance path is based on map data identifying a marking type for a traveling lane such that the path crosses the lane when the marking type is broken and does not cross the lane when the marking type is solid.

Abstract: Various embodiments of the present disclosure provide a vehicle notification system. The vehicle notification system includes at least one transparent display device, at least one mirror display device, and an eye tracker, and is configured to determine which display device the vehicle's driver is looking at and display notifications on that display device.

Abstract: A vehicle includes a steering wheel and a controller. The controller is configured to, in response to receiving location and speed data from other vehicles indicating an expected collision absent a trajectory change, automatically control the steering wheel to direct the vehicle along a collision avoidance path. The collision avoidance path is based on map data identifying a marking type for a traveling lane such that the path crosses the lane when the marking type is broken and does not cross the lane when the marking type is solid.

Abstract: A vehicle is described that includes a video camera configured to record video, a processor configured to process the recorded video from the video camera into hyperlapse video, and an input/output device to link the hyperlapse video with a social network associated with at least one of a driver of the vehicle and the vehicle. The processor may be configured to receive video data from the video camera and to create the hyperlapse video by reconstructing a video stream from the video data, planning a smooth path through the video stream, and rendering the video data along the smooth path into the hyperlapse video that has a faster speed and less jitter than the video stream. The hyperlapse video may be a non-native, sped-up video of the recorded video from the video camera.

Abstract: A crowdsourcing server receives sensor data from vehicles within a vicinity of a location of an event at a time of the event in response to the vehicles being notified of an occurrence of the event. The sensor data includes sensor data of the vehicles sensed at the time of the event. The crowdsourcing server associates the sensor data with the event. The event can be reconstructed using the sensor data associated with the event.