  We investigate methods to provide safety assurances for autonomous agents
that incorporate predictions of other, uncontrolled agents' behavior into their
own trajectory planning. Given a learning-based forecasting model that predicts
agents' trajectories, we introduce a method for providing probabilistic
assurances on the model's prediction error with calibrated confidence
intervals. Through quantile regression, conformal prediction, and reachability
analysis, our method generates probabilistically safe and dynamically feasible
prediction sets. We showcase their utility in certifying the safety of planning
algorithms, both in simulations using actual autonomous driving data and in an
experiment with Boeing vehicles.
