Abstract: In various examples, multimodal image data may be used to generate a set of top-down tile images, which are applied to a deep neural network generator architecture model to produce lane marking-specific heatmap images corresponding to the set of top-down tile images. The multimodal sensor data may include LIDAR-captured intensity channel data, LIDAR-captured feature height channel data, and optical color image channel data. The set of top-down tile images may be processed by the generator model to automatically detect lane boundaries and navigation boundaries to generate pixel-level heatmap images that may classify lane markings by marking characteristics such as line type and/or color. The generator model may comprise an encoder-decoder architecture, with multiscale feature extraction and/or context extraction functional layers intervening between the encoder model and the decoder model.

Abstract: In various examples, feature location identification for autonomous and semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use LiDAR data to determine locations of road markings within an environment. For instance, the LiDAR data may be used to generate one or more images, such as a top-down image, birds-eye-view (BEV) image, and/or an intensity image, representing the environment. The image(s) may then be processed using one or more machine learning models that are configured to determine information associated with the road markings, such as bounding shapes (e.g., bounding boxes) indicating the locations of the road markings within the image(s), classifications associated with the road markings (e.g., the types of road markings), and/or any other information. The information may then be used to perform one or more processes, such as updating a map of the environment and/or navigating a vehicle within the environment.

Abstract: In various examples, determining locations of road markings or other features for autonomous and semi-autonomous systems and applications is described. Systems and methods are disclosed that use LiDAR data—and/or other sensor data types—to determine the locations of road markings, such as lane lines, within environments. For instance, sensor data, such as image data, may initially be processed in order to determine an initial location associated with a road marking. The LiDAR data may then be used to increase the precision of the initial location associated with the road marking. For example, a LiDAR image(s) may be used to determine the actual location of the road marking, such as the center of paint associated with a lane line. Additionally, the initial location of the road marking may be updated, such as adjusted within a direction, based on the actual location of the road marking.

Abstract: In various examples, intersections may be identified using sensor data for mapping in autonomous or semi-autonomous systems and applications. Systems and methods are disclosed that receive sensor data, such as image data, generated using one or more sensor of one or more vehicles navigating within an environment. The systems and methods may then use the sensor data to determine the locations and/or layouts of intersections within the environment and update a map to indicate the locations and/or layouts. For instance, the sensor data may be analyzed to detect the locations of the intersections, such as the locations of the boundaries of the intersections for which the vehicles navigated through. The map may then be updated to indicate the locations of the intersections by indicating the locations of the boundaries within the map, such as by using bounding shapes that are generated based on the locations of the boundaries.

Abstract: A glass-type device which controls the output position of an image by using the location of the user's eye. The glass-type device includes: a display portion; a mirror portion that is placed on the path of light output from the display portion so as to reflect an image output on the display portion to an external object; a sensing portion that is placed on the path of light coming from the external object and reflected by the mirror portion so as to sense an image of the external object; and a controller that controls the output image by using the sensed image.

Abstract: A glass-type device which controls the output position of an image by using the location of the user's eye. The glass-type device includes: a display portion; a mirror portion that is placed on the path of light output from the display portion so as to reflect an image output on the display portion to an external object; a sensing portion that is placed on the path of light coming from the external object and reflected by the mirror portion so as to sense an image of the external object; and a controller that controls the output image by using the sensed image.