Abstract: Apparatus and methods of the present disclosure provide a solution to quickly sort through and identify relevant data sets from large amounts of data collected by numerous vehicles. This process is a form of data mining where a processor executes instructions out of a memory to evaluate sensor data collected by vehicles over time. These vehicles may be driven by person's such that data collected by those sensors would be representative of how a person drives. After data has been acquired, it may be evaluated to identify portions of data where one vehicle encounters another vehicle along a roadway. Data may be classified as being associated with particular types of driving maneuvers and organized into data sets with labels consistent with the classification or driving maneuver. Classifications could correspond to different typed of driving maneuvers, “turning left across traffic,” “all way stop,” “merging onto a highway,” or “lane change”.

Abstract: An electronic device housing, an electronic device and a compound body are provided. The electronic device housing comprises a frame; a sealing layer, disposed on at least a part of an outer surface of the frame, and including a plurality of sub-sealing layers laminated in sequence; and a back case, attached to the frame by the sealing layer, wherein two adjacent sub-sealing layers have different compositions.

Abstract: A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Abstract: A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Abstract: Apparatus and methods of the present disclosure provide a solution to quickly sort through and identify relevant data sets from large amounts of data collected by numerous vehicles. This process is a form of data mining where a processor executes instructions out of a memory to evaluate sensor data collected by vehicles over time. These vehicles may be driven by person's such that data collected by those sensors would be representative of how a person drives. After data has been acquired, it may be evaluated to identify portions of data where one vehicle encounters another vehicle along a roadway. Data may be classified as being associated with particular types of driving maneuvers and organized into data sets with labels consistent with the classification or driving maneuver. Classifications could correspond to different typed of driving maneuvers, “turning left across traffic,” “all way stop,” “merging onto a highway,” or “lane change.

Abstract: A method for providing an anti-rollback secure timer service includes determining, at a device which includes a processor providing a trusted execution environment (TEE), a trusted memory, and a real time clock (RTC) accessible through an operating system of the device, an initial reference time value, by a secure timer application running in the TEE, the initial reference time value determined based on an initial value of the RTC obtained during booting of the device and a time delta value. The method further includes determining an updated reference time value based on the initial reference time value, a second value of the RTC, and a previously stored old reference time value, determining an updated time delta value based on the second value of the RTC and the updated reference time value, and storing the updated time delta value and the updated reference time value in the trusted memory.

Abstract: A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Abstract: A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Abstract: A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Abstract: An electronic device housing, an electronic device and a compound body are provided. The electronic device housing comprises a frame; a sealing layer, disposed on at least a part of an outer surface of the frame, and including a plurality of sub-sealing layers laminated in sequence; and a back case, attached to the frame by the sealing layer, wherein two adjacent sub-sealing layers have different compositions.

Abstract: A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Abstract: A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Abstract: A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Abstract: A method for providing an anti-rollback secure timer service includes determining, at a device which includes a processor providing a trusted execution environment (TEE), a trusted memory, and a real time clock (RTC) accessible through an operating system of the device, an initial reference time value, by a secure timer application running in the TEE, the initial reference time value determined based on an initial value of the RTC obtained during booting of the device and a time delta value. The method further includes determining an updated reference time value based on the initial reference time value, a second value of the RTC, and a previously stored old reference time value, determining an updated time delta value based on the second value of the RTC and the updated reference time value, and storing the updated time delta value and the updated reference time value in the trusted memory.

Abstract: In an embodiment, a first media presentation session is established between a first Source and the Sink, and a second media presentation session between a second Source and the Sink. First and second media streams are streamed to the Sink by the first and second Sources, respectively. The Sink presents the first and second media streams in respective portions of a display screen, and detects user input at the Sink that is indicative of a trigger to transfer data originated from the first Source to the second Source. The Sink coordinates with one or both of the first Source and the second Source to facilitate the data to be delivered to the second Source in response to the detection. As a result of the coordination, the second Source receives the first Source-originated data (e.g., from the first Source directly, or via the Sink).

Abstract: Aspects of the present disclosure relate to graphics domain transmission methods that utilize an adaptive compression pipeline to achieve low latency screen mirroring between a source device and a sink device. A source device captures a plurality of graphics domain frames, each of the graphics domain frames including one or more graphics command tokens. The source device utilizes an adaptive compression pipeline to compress the graphics domain frames based on one or more characteristics of the frames, and the adaptive compression pipeline is configured to perform at least one of scalable texture streaming, frame-based prediction, frame dropping, or data compression. The source device transmits the compressed frames to a sink device, and displays a rendered image of the graphics domain frames in time synchronization with a corresponding rendered image of the compressed frames displayed at the sink device.