Abstract: One embodiment of a virtual reality apparatus comprises: a graphics processing engine comprising a plurality of graphics processing stages, the graphics processing engine to render a plurality of image frames for left and right displays of a head mounted display (HMD); and foveation control hardware logic to independently control two or more of the plurality of graphics processing stages based on feedback received from an eye tracking module of the HMD, the feedback indicating a foveated region selected based on a current or anticipated direction of a user's gaze, the foveation control hardware logic to cause the two or more of the graphics processing stages to process the foveated region differently than other regions of the image frames.

Abstract: Described herein is a technique in which a plurality of distortion meshes compensate for radial and chromatic aberrations created by optical lenses. The plurality of distortion meshes may include different lens specific parameters that allow the distortion meshes to compensate for chromatic aberrations created within received images. The plurality of distortion meshes may correspond to a red color channel, green color channel, or blue color channel to compensate for the chromatic aberrations. The distortion meshes may also include shaped distortions and grids to compensate for radial distortions, such as pin cushion distortions. In one example, the system uses a barrel-shaped distortion and a triangulation grid to compensate for the distortions created when the received image is displayed on a lens.

Abstract: Herein is disclosed an infrastructure state prediction device comprising a memory device onto which an infrastructure state model corresponding to a location and a state timing of a plurality of infrastructure elements is stored; one or more processors, communicatively coupled to the memory device and configured to receive measurement information representing a location and an observed state of a first infrastructure element; determine a predicted state of a second infrastructure element based on a state timing corresponding to a second infrastructure element; determine a movement information based on the observed state of the first infrastructure element, the predicted state of the second infrastructure element and a relation between the location of the first infrastructure element and a location of the second infrastructure element; and generate a message comprising the movement information.

Abstract: A sensor calibrator comprising one or more processors configured to receive sensor data representing a calibration pattern detected by a sensor during a period of relative motion between the sensor and the calibration pattern in which the sensor or the calibration pattern move along a linear path of travel; determine a calibration adjustment from the plurality of images; and send a calibration instruction for calibration of the sensor according to the determined calibration adjustment. Alternatively, a sensor calibration detection device, comprising one or more processors, configured to receive first sensor data detected during movement of a first sensor along a route of travel; determine a difference between the first sensor data and stored second sensor data; and if the difference is outside of a predetermined range, switch from a first operational mode to a second operational mode.

Abstract: An infrastructure state prediction device includes a processor configured to receive sensor information from a sensor at an infrastructure element, wherein the sensor information includes an observation of a traffic object at the infrastructure element; and determine, based on the sensor information and an infrastructure state model comprising information indicative of a state timing for the infrastructure element, an updated state timing for the infrastructure element; and a transmitter configured to transmit the updated state timing to the infrastructure element.

Abstract: Described herein is a technique in which a plurality of distortion meshes compensate for radial and chromatic aberrations created by optical lenses. The plurality of distortion meshes may include different lens specific parameters that allow the distortion meshes to compensate for chromatic aberrations created within received images. The plurality of distortion meshes may correspond to a red color channel, green color channel, or blue color channel to compensate for the chromatic aberrations. The distortion meshes may also include shaped distortions and grids to compensate for radial distortions, such as pin cushion distortions. In one example, the system uses a barrel-shaped distortion and a triangulation grid to compensate for the distortions created when the received image is displayed on a lens.

Abstract: A device and a method for determining a format of an immersive image and/or for rendering an immersive image are disclosed, wherein the device for determining a format of an immersive image includes one or more processors configured to: determine an aspect ratio of an immersive image and one or more additional characteristics of the immersive image; and determine a format of the immersive image using the determined aspect ratio and the determined one or more additional characteristics of the immersive image.

Abstract: Herein is disclosed a detection device comprising one or more sensors, configured to receive sensor input from a vicinity of a first vehicle, and to generate sensor data representing the received sensor input; one or more processors, configured to detect a second vehicle from the received sensor data; determine from the sensor data a region of a first type relative to the second vehicle and a region of a second type relative to the second vehicle; and control the first vehicle to avoid or reduce travel in the one or more regions of the first type or to travel from a region of the first type to a region of the second type.

Abstract: Apparatus and method for correcting image regions following upsampling or frame interpolation. For example, one embodiment of an apparatus comprises a machine-learning engine to evaluate at least a first image in a sequence of images generated by a real-time interactive application, the machine learning engine to responsively use previously learned data to generate an upsampled or interpolated image comprising a plurality of pixel patches. In one embodiment, each pixel patch is associated with a confidence value reflecting how accurately the pixel patch was generated by the machine learning engine. A selective ray tracing engine identifies a first pixel patch to be corrected based a first confidence value corresponding to the first pixel patch being lower than a threshold and performs ray tracing operations on a first portion of the first image to generate a corrected first pixel patch.

Abstract: Autonomous unmanned vehicles (UVs) for responding to situations are described. Embodiments include UVs that launch upon detection of a situation, operate in the area of the situation, and collect and send information about the situation. The UVs may launch from a vehicle involved in the situation, a vehicle responding to the situation, or from a fixed station. In other embodiments, the UVs also provide communications relays to the situation and may facilitate access to the situation by responders. The UVs further may act as decoupled sensors for vehicles. In still other embodiments, the collected information may be used to recreate the situation as it happened.

Abstract: Described herein is a technique in which a plurality of distortion meshes compensate for radial and chromatic aberrations created by optical lenses. The plurality of distortion meshes may include different lens specific parameters that allow the distortion meshes to compensate for chromatic aberrations created within received images. The plurality of distortion meshes may correspond to a red color channel, green color channel, or blue color channel to compensate for the chromatic aberrations. The distortion meshes may also include shaped distortions and grids to compensate for radial distortions, such as pin cushion distortions. In one example, the system uses a barrel-shaped distortion and a triangulation grid to compensate for the distortions created when the received image is displayed on a lens.

Abstract: According to various aspects, a controller includes one or more processors configured to: determine when a predicted event occurs, at which a velocity of a vehicle is changed; and provide an instruction for a reorientation of one or more seats of the vehicle based on when the predicted event occurs.

Abstract: An apparatus to facilitate deep learning based selection of samples for adaptive supersampling is disclosed. The apparatus includes one or more processing elements to: receive training data comprising input tiles and corresponding supersampling values for the input tiles, wherein each input tile comprises a plurality of pixels, and train, based on the training data, a machine learning model to identify a level of supersampling for a rendered tile of pixels.

Abstract: Systems, apparatuses, and methods may provide for technology to dynamically control a display in response to ocular characteristic measurements of at least one eye of a user.

Abstract: According to various aspects, an obstacle analyzer may include: one or more sensors configured to receive obstacle identification information representing one or more identification features of an obstacle and obstacle condition information associated with one or more conditions of the obstacle; and one or more processors configured to identify the obstacle based on the received obstacle identification information and generate an identification value corresponding to the identified obstacle, determine a rating value representing a risk potential of the identified obstacle based on the received obstacle condition information, and store the rating value assigned to the identification value of the identified obstacle in one or more memories.

Abstract: Methods, systems and apparatuses may include technology that compresses a central region of an image to a central level of detail and compresses one or more peripheral regions of the image to one or more peripheral levels of detail that are less than the central level of detail, wherein the central region and the peripheral region(s) are fixed. Additionally, the technology may send the compressed central region and the compressed peripheral region(s) to a remote display. In one example, the central region and the peripheral region(s) are independent of eye movement with respect to the remote display.

Abstract: Apparatus and method for correcting image regions following upsampling or frame interpolation. For example, one embodiment of an apparatus comprises a machine-learning engine to evaluate at least a first image in a sequence of images generated by a real-time interactive application, the machine learning engine to responsively use previously learned data to generate an upsampled or interpolated image comprising a plurality of pixel patches. In one embodiment, each pixel patch is associated with a confidence value reflecting how accurately the pixel patch was generated by the machine learning engine. A selective ray tracing engine identifies a first pixel patch to be corrected based a first confidence value corresponding to the first pixel patch being lower than a threshold and performs ray tracing operations on a first portion of the first image to generate a corrected first pixel patch.

Abstract: Systems and methods may provide a plurality of distortion meshes that compensate for radial and chromatic aberrations created by optical lenses. The plurality of distortion meshes may include different lens specific parameters that allow the distortion meshes to compensate for chromatic aberrations created within received images. The plurality of distortion meshes may correspond to a red color channel, green color channel, or blue color channel to compensate for the chromatic aberrations. The distortion meshes may also include shaped distortions and grids to compensate for radial distortions, such as pin cushion distortions. In one example, the system uses a barrel-shaped distortion and a triangulation grid to compensate for the distortions created when the received image is displayed on a lens.

Abstract: One embodiment of a virtual reality apparatus comprises: a graphics processing engine comprising a plurality of graphics processing stages, the graphics processing engine to render a plurality of image frames for left and right displays of a head mounted display (HMD); and foveation control hardware logic to independently control two or more of the plurality of graphics processing stages based on feedback received from an eye tracking module of the HMD, the feedback indicating a foveated region selected based on a current or anticipated direction of a user's gaze, the foveation control hardware logic to cause the two or more of the graphics processing stages to process the foveated region differently than other regions of the image frames.

Abstract: An apparatus, method and computer readable medium for a voice-controlled camera with artificial intelligence (AI) for precise focusing. The method includes receiving, by the camera, natural language instructions from a user for focusing the camera to achieve a desired photograph. The natural language instructions are processed using natural language processing techniques to enable the camera to understand the instructions. A preview image of a user desired scene is captured by the camera. Artificial Intelligence (AI) is applied to the preview image to obtain context and to detect objects within the preview image. A depth map of the preview image is generated to obtain distances from the detected objects in the preview image to the camera. It is determined whether the detected objects in the image match the natural language instructions from the user.