Abstract: Embodiments of the present disclosure relate to encoding of junction area information in map data. In particular, the encoding may include organizing vehicle paths that traverse through a junction area according to path groups and organizing contentions that influence behavior of vehicles traveling along the vehicle paths according to contention groups. In addition, the encoding may include generating direction data structures that associate respective path groups with one or more of the contention groups. In these or other embodiments, the map data that corresponds to the junction area may be updated with direction data structures.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: In various examples, a method includes computing a current keyframe, the current keyframe being representative of an area around an autonomous vehicle at a current time based on map data. The method includes transforming a preceding keyframe to a coordinate frame of the autonomous vehicle at a first time prior to completing computation of the current keyframe to generate a first world model frame. The method includes transforming the preceding keyframe to the coordinate frame of the autonomous vehicle at a second time after the first time and prior to completing computation of the current keyframe to generate a second world model frame.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: In examples, autonomous vehicles are enabled to negotiate yield scenarios in a safe and predictable manner. In response to detecting a yield scenario, a wait element data structure is generated that encodes geometries of an ego path, a contender path that includes at least one contention point with the ego path, as well as a state of contention associated with the at least on contention point. Geometry of yield scenario context may also be encoded, such as inside ground of an intersection, entry or exit lines, etc. The wait element data structure is passed to a yield planner of the autonomous vehicle. The yield planner determines a yielding behavior for the autonomous vehicle based at least on the wait element data structure. A control system of the autonomous vehicle may operate the autonomous vehicle in accordance with the yield behavior, such that the autonomous vehicle safely negotiates the yield scenario.

Abstract: In various examples, a high definition (HD) map is provided that includes a segmented data structure that allows for selective access to desired road segments and corresponding layers of map data. For example, the HD map may be segmented into a series of tiles that may correspond to a geographic region, and each of the tiles may include any number of road segments corresponding to portions of the geographic region. Each road segment may include a corresponding set of layers—which may include driving layers for use by the ego-machine and/or training layers for generating ground truth data—from the HD map that are associated with the road segment alone. As such, when traversing the environment, an ego-machine may determine one or more road segments within a tile corresponding to a current location, and may selectively download one or more layers for each of the one or more road segments.

Abstract: In various examples, health of a high definition (HD) map may be monitored to determine whether inaccuracies exist in one or more layers of the HD map. For example, as one or more vehicles rely on the HD map to traverse portions of an environment, disagreements between perception of the one or more vehicles, map layers of the HD map, and/or other disagreement types may be identified and aggregated. Where errors are identified that indicate a drop in health of the HD map, updated data may be crowdsourced from one or more vehicles corresponding to a location of disagreement within the HD map, and the updated data may be used to update, verify, and validate the HD map.

Abstract: Systems and methods for vehicle-based determination of HD map update information. Sensor-equipped vehicles may determine locations of various detected objects relative to the vehicles. Vehicles may also determine the location of reference objects relative to the vehicles, where the location of the reference objects in an absolute coordinate system is also known. The absolute coordinates of various detected objects may then be determined from the absolute position of the reference objects and the locations of other objects relative to the reference objects. Newly-determined absolute locations of detected objects may then be transmitted to HD map services for updating.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: Disclosed are an apparatus and a method of low-latency, low-power eye tracking. In some embodiments, the eye tracking method operates a first sensor having a first level of power consumption that tracks positions of an eye of a user. In response to detection that the eye does not change position for a time period, the method stops operation of the first sensor and instead operates a second sensor that detects a change of position of the eye. The second sensor has a level of power consumption lower than the level of power consumption of the first sensor. Once the eye position changes, the second sensor resumes operation.

Abstract: Systems and methods for controlling closed captioning using an eye tracking device are provided. The system for controlling closed captioning may comprise a display device, a closed captioning controller configured to display closed captioning text for a media item during playback on the display device, and an eye tracking device configured to detect a location of a user's gaze relative to the display device and send the location to the closed captioning controller. The closed captioning controller may be configured to recognize a predetermined gaze pattern of the user's gaze and, upon detecting the predetermined gaze pattern, partially or completely deemphasize the display of the closed captioning text.

Abstract: Examples relating to calibrating an estimated gaze location are disclosed. One example method comprises monitoring the estimated gaze location of a viewer using gaze tracking data from a gaze tracking system. Image data for display via a display device is received, the image data comprising at least one target visual and target visual metadata that identifies the at least one target visual. The target visual metadata is used to identify a target location of the at least one target visual. The estimated gaze location of the viewer is monitored and a probability that the viewer is gazing at the target location is estimated. The gaze tracking system is calibrated using the probability, the estimated gaze location and the target location to generate an updated estimated gaze location of the viewer.

Abstract: Embodiments are disclosed herein that relate to selecting user interface elements via a periodically updated position signal. For example, one disclosed embodiment provides a method comprising displaying on a graphical user interface a representation of a user interface element and a representation of an interactive target. The method further comprises receiving an input of coordinates of the periodically updated position signal, and determining a selection of the user interface element if a motion interaction of the periodically updated position signal with the interactive target meets a predetermined motion condition.

Abstract: Disclosed are an apparatus and a method of low-latency, low-power eye tracking. In some embodiments, the eye tracking method operates a first sensor having a first level of power consumption that tracks positions of an eye of a user. In response to detection that the eye does not change position for a time period, the method stops operation of the first sensor and instead operates a second sensor that detects a change of position of the eye. The second sensor has a level of power consumption lower than the level of power consumption of the first sensor. Once the eye position changes, the second sensor resumes operation.

Abstract: A method to furnish input representing gaze direction in a computer system operatively coupled to a vision system. In this method, a first image of an eye at a first level of illumination is acquired by a camera of the vision system. The first image is obtained from the camera, and a second image of the eye corresponding to a second, different level of illumination is also obtained. Brightness of corresponding pixels of the first and second images is compared in order to distinguish a reflection of the illumination by the eye from a reflection of the illumination by eyewear. The input is then furnished based on the reflection of the illumination by the eye.

Abstract: Embodiments are disclosed for eye tracking systems and methods. An example eye tracking system comprises a plurality of light sources and a camera configured to capture an image of light from the light sources as reflected from an eye. The eye tracking system further comprises a logic device and a storage device storing instructions executable by the logic device to acquire frames of eye tracking data by iteratively projecting light from different combinations of light sources of the plurality of light sources and capturing an image of the eye during projection of each combination. The instructions may be further executable to select a selected combination of light sources for eye tracking based on a determination of occlusion detected in the image arising from a transparent or semi-transparent optical structure positioned between the eye and the camera and project light via the selected combination of light sources for eye tracking.

Abstract: Examples relating to calibrating an estimated gaze location are disclosed. One example method comprises monitoring the estimated gaze location of a viewer using gaze tracking data from a gaze tracking system. Image data for display via a display device is received, the image data comprising at least one target visual and target visual metadata that identifies the at least one target visual. The target visual metadata is used to identify a target location of the at least one target visual. The estimated gaze location of the viewer is monitored and a probability that the viewer is gazing at the target location is estimated. The gaze tracking system is calibrated using the probability, the estimated gaze location and the target location to generate an updated estimated gaze location of the viewer.

Abstract: Examples relating calibrating an estimated gaze location are disclosed. One example method comprises monitoring the estimated gaze location of a viewer using gaze tracking data from a gaze tracking system. Image data for display via a display device is received and, without using input from the viewer, at least one target visual that may attract a gaze of the viewer and a target location of the target visual are identified within the image data. The estimated gaze location of the viewer is compared with the target location of the target visual. An offset vector is calculated based on the estimated gaze location and the target location. The gaze tracking system is calibrated using the offset vector.