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Collision prediction in a dynamic and unknown environment relies on knowledgeof how the environment is changing. Many collision prediction methods rely ondeterministic knowledge of how obstacles are moving in the environment.However, complete deterministic knowledge of the obstacles' motion is oftenunavailable. This work proposes a Gaussian process based prediction method thatreplaces the assumption of deterministic knowledge of each obstacle's futurebehavior with probabilistic knowledge, to allow a larger class of obstacles tobe considered. The method solely relies on position and velocity measurementsto predict collisions with dynamic obstacles. We show that the uncertaintyregion for obstacle positions can be expressed in terms of a combination ofpolynomials generated with Gaussian process regression. To control the growthof uncertainty over arbitrary time horizons, a probabilistic obstacle intentionis assumed as a distribution over obstacle positions and velocities, which canbe naturally included in the Gaussian process framework. Our approach isdemonstrated in two case studies in which (i), an obstacle overtakes the agentand (ii), an obstacle crosses the agent's path perpendicularly. In thesesimulations we show that the collision can be predicted despite having limitedknowledge of the obstacle's behavior.

Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification