Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating realistic full-scene point clouds. One of the methods includes obtaining an initial scene point cloud characterizing an initial scene in an environment; obtaining, for each of one or more objects, an object point cloud that characterizes the object; and processing a first input comprising the initial scene point cloud and the one or more object point clouds using a first neural network that is configured to process the first input to generate a final scene point cloud that characterizes a transformed scene that has the one or more objects added to the initial scene.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating realistic full-scene point clouds. One of the methods includes obtaining an initial scene point cloud characterizing an initial scene in an environment; obtaining, for each of one or more objects, an object point cloud that characterizes the object; and processing a first input comprising the initial scene point cloud and the one or more object point clouds using a first neural network that is configured to process the first input to generate a final scene point cloud that characterizes a transformed scene that has the one or more objects added to the initial scene.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating realistic full-scene point clouds. One of the methods includes obtaining an initial scene point cloud characterizing an initial scene in an environment; obtaining, for each of one or more objects, an object point cloud that characterizes the object; and processing a first input comprising the initial scene point cloud and the one or more object point clouds using a first neural network that is configured to process the first input to generate a final scene point cloud that characterizes a transformed scene that has the one or more objects added to the initial scene.

Abstract: A method for controlling an ego agent includes periodically receiving policy information comprising a spatial environment observation and a current state of the ego agent. The method also includes selecting, for each received policy information, a low-level policy from a number of low-level policies. The low-level policy may be selected based on a high-level policy. The method further includes controlling an action of the ego agent based on the selected low-level policy.

Abstract: A system and method for predicting pedestrian intent is provided. A prediction circuit comprising a plurality of gated recurrent units (GRUB) receives a sequence of images captured by a camera. The prediction circuit parses each frame of the sequence of images to identify one or more pedestrians and one or more objects. Using the parsed data, the prediction circuit generates a pedestrian-centric spatiotemporal graph, the parsed data comprising one or more identified pedestrians and one or more identified object. The prediction circuit uses the pedestrian-centric graph to determine a probability of one or more pedestrians crossing a street for each frame of the sequence of images.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating realistic full-scene point clouds. One of the methods includes obtaining an initial scene point cloud characterizing an initial scene in an environment; obtaining, for each of one or more objects, an object point cloud that characterizes the object; and processing a first input comprising the initial scene point cloud and the one or more object point clouds using a first neural network that is configured to process the first input to generate a final scene point cloud that characterizes a transformed scene that has the one or more objects added to the initial scene.

Abstract: A system and method for predicting pedestrian intent is provided. A prediction circuit comprising a plurality of gated recurrent units (GRUB) receives a sequence of images captured by a camera. The prediction circuit parses each frame of the sequence of images to identify one or more pedestrians and one or more objects. Using the parsed data, the prediction circuit generates a pedestrian-centric spatiotemporal graph, the parsed data comprising one or more identified pedestrians and one or more identified object. The prediction circuit uses the pedestrian-centric graph to determine a probability of one or more pedestrians crossing a street for each frame of the sequence of images.