Abstract: Methods, systems, and apparatus for predicting future trajectories of agents in an environment. In one aspect, a system comprises one or more computers configured to receive a data set comprising multiple training examples. The training examples include scene data comprising respective agent data for multiple agents and a ground truth trajectory for a target agent that represents ground truth motion of the target agent after a corresponding time point. The one or more computers obtain data identifying one or more of the multiple agents as non-causal agents for each training example. A non-causal agent is an agent whose states do not cause the ground truth trajectory for the target agent to change. The one or more computers generate a respective modified training example from each of the multiple training examples.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating a behavior prediction system.

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction that characterizes an environment. The system obtains an input including data characterizing observed trajectories one or more agents and data characterizing one or more map features identified in a map of the environment. The system generates, from the input, an encoder input that comprises representations for each of a plurality of points in a top-down representation of the environment. The system processes the encoder input using a point cloud encoder neural network to generate a global feature map of the environment, and processes a prediction input including the global feature map using a predictor neural network to generate a prediction output characterizing the environment.