Abstract: A surround multi-object tracking and surround vehicle motion prediction framework is provided. A full-surround camera array and LiDAR sensor based approach provides for multi-object tracking for autonomous vehicles. The multi-object tracking incorporates a fusion scheme to handle object proposals from the different sensors within the calibrated camera array. A motion prediction framework leverages the instantaneous motion of vehicles, an understanding of motion patterns of freeway traffic, and the effect of inter-vehicle interactions. The motion prediction framework incorporates probabilistic modeling of surround vehicle trajectories. Additionally, subcategorizing trajectories based on maneuver classes leads to better modeling of motion patterns. A model takes into account interactions between surround vehicles for simultaneously predicting each of their motion.

Abstract: Methods, systems, and computer program products for gathering electronic signatures to be applied to collaboration system content objects (e.g., contracts, letters, insurance claims, riders, etc.). A collaboration system monitors changes made to the collaboration system content objects during electronic signature processing. A module of the content management system is configured to associate one or more instances of e-signing metadata to one or more of the stored content objects of the content management system. The e-signing metadata indicates whether or not a particular portion of the one or more of the stored content objects has been e-signed by a designated e-signatory. A collaborator who is not one of the designated e-signatories makes a change to one or more of the stored content objects (e.g., contracts, letters, insurance claims, riders, etc.). The change is remediated on-the-fly and the e-signing process continues without having to restart the e-signing process from the beginning.

Abstract: Provided are methods for predicting agent trajectories, which can include generating a graph corresponding to a map of a scene by encoding map features and agent features as node encodings of the graph and determining a policy for application to outgoing edges of the nodes of the graph. Some methods described also include sampling paths for a target vehicle in the scene according to the policy and predicting a set of trajectories based on the sampled paths traversed by the policy and a sampled latent variable. Systems and computer program products are also provided.

Abstract: A surround multi-object tracking and surround vehicle motion prediction framework is provided. A full-surround camera array and LiDAR sensor based approach provides for multi-object tracking for autonomous vehicles. The multi-object tracking incorporates a fusion scheme to handle object proposals from the different sensors within the calibrated camera array. A motion prediction framework leverages the instantaneous motion of vehicles, an understanding of motion patterns of freeway traffic, and the effect of inter-vehicle interactions. The motion prediction framework incorporates probabilistic modeling of surround vehicle trajectories. Additionally, subcategorizing trajectories based on maneuver classes leads to better modeling of motion patterns. A model takes into account interactions between surround vehicles for simultaneously predicting each of their motion.