Probabilistic Prediction of Dynamic Object Behavior for Autonomous Vehicles

Systems and methods are described that probabilistically predict dynamic object behavior. In particular, in contrast to existing systems which attempt to predict object trajectories directly (e.g., directly predict a specific sequence of well-defined states), a probabilistic approach is instead leveraged that predicts discrete probability distributions over object state at each of a plurality of time steps. In one example, systems and methods predict future states of dynamic objects (e.g., pedestrians) such that an autonomous vehicle can plan safer actions/movement.