Abstract: A computing system can input first relative location embedding data into an interaction transformer model and receive, as an output of the interaction transformer model, motion forecast data for actors relative to a vehicle. The computing system can input the motion forecast data into a prediction model to receive respective trajectories for the actors for a current time step and respective projected trajectories for the actors for a subsequent time step. The computing system can generate second relative location embedding data based on the respective projected trajectories from the second time step. The computing system can produce second motion forecast data using the interaction transformer model based on the second relative location embedding. The computing system can determine second respective trajectories for the actors using the prediction model based on the second forecast data.

Abstract: Aspects of the present disclosure involve systems, methods, and devices for autonomous vehicle localization using a Lidar intensity map. A system is configured to generate a map embedding using a first neural network and to generate an online Lidar intensity embedding using a second neural network. The map embedding is based on input map data comprising a Lidar intensity map, and the Lidar sweep embedding is based on online Lidar sweep data. The system is further configured to generate multiple pose candidates based on the online Lidar intensity embedding and compute a three-dimensional (3D) score map comprising a match score for each pose candidate that indicates a similarity between the pose candidate and the map embedding. The system is further configured to determine a pose of a vehicle based on the 3D score map and to control one or more operations of the vehicle based on the determined pose.

Abstract: A method includes receiving image data associated with an image of a roadway including a crosswalk, generating a plurality of different characteristics of the image based on the image data, determining a position of the crosswalk on the roadway based on the plurality of different characteristics, the position including a first boundary and a second boundary of the crosswalk in the roadway, and providing map data associated with a map of the roadway, the map data including the position of the crosswalk on the roadway in the map. The plurality of different characteristics include a classification of one or more elements of the image, a segmentation of the one or more elements of the image, and one or more angles of the one or more elements of the image with respect to a line in the roadway.

Abstract: A computer system including one or more processors programmed or configured to receive image data associated with an image of one or more roads, where the one or more roads comprise one or more lanes, determine a lane classification of the one or more lanes based on the image data associated with the image of the one or more roads, and provide lane classification data associated with the lane classification of the one or more lanes.

Abstract: A method includes receiving image data associated with an image of a roadway including a crosswalk, generating a plurality of different characteristics of the image based on the image data, determining a position of the crosswalk on the roadway based on the plurality of different characteristics, the position including a first boundary and a second boundary of the crosswalk in the roadway, and providing map data associated with a map of the roadway, the map data including the position of the crosswalk on the roadway in the map. The plurality of different characteristics include a classification of one or more elements of the image, a segmentation of the one or more elements of the image, and one or more angles of the one or more elements of the image with respect to a line in the roadway.

Abstract: Aspects of the present disclosure involve systems, methods, and devices for autonomous vehicle localization using a Lidar intensity map. A system is configured to generate a map embedding using a first neural network and to generate an online Lidar intensity embedding using a second neural network. The map embedding is based on input map data comprising a Lidar intensity map, and the Lidar sweep embedding is based on online Lidar sweep data. The system is further configured to generate multiple pose candidates based on the online Lidar intensity embedding and compute a three-dimensional (3D) score map comprising a match score for each pose candidate that indicates a similarity between the pose candidate and the map embedding. The system is further configured to determine a pose of a vehicle based on the 3D score map and to control one or more operations of the vehicle based on the determined pose.

Abstract: A method includes receiving image data associated with an image of a roadway including a crosswalk, generating a plurality of different characteristics of the image based on the image data, determining a position of the crosswalk on the roadway based on the plurality of different characteristics, the position including a first boundary and a second boundary of the crosswalk in the roadway, and providing map data associated with a map of the roadway, the map data including the position of the crosswalk on the roadway in the map. The plurality of different characteristics include a classification of one or more elements of the image, a segmentation of the one or more elements of the image, and one or more angles of the one or more elements of the image with respect to a line in the roadway.

Abstract: A method includes obtaining training data including one or more images and one or more ground truth labels of the one or more images, and training an adversarial network including a siamese discriminator network and a generator network. The training includes generating, with the generator network, one or more generated images based on the one or more images; processing, with the siamese discriminator network, at least one pair of images to determine a prediction of whether the at least one pair of images includes the one or more generated images; and modifying, using a loss function of the adversarial network that depends on the ground truth label and the prediction, one or more parameters of the generator network.

Abstract: A computer system including one or more processors programmed or configured to receive image data associated with an image of one or more roads, where the one or more roads comprise one or more lanes, determine a lane classification of the one or more lanes based on the image data associated with the image of the one or more roads, and provide lane classification data associated with the lane classification of the one or more lanes.