RAP: Risk-Aware Prediction for Robust Planning

In safety-critical and interactive control tasks such as autonomous driving, the robot must successfully account for uncertainty of the future motion of surrounding humans. To achieve this, many contemporary approaches decompose the decision-making pipeline into prediction and planning modules [1-5] for maintainability, debuggability, and interpretability. A prediction module, often learned from data, first produces likely future trajectories of surrounding agents, which are then consumed by a planning module for computing safe robot actions. Recent works [6, 7] further propose to couple prediction with risk-sensitive planning for enhanced safety, wherein the planner computes and minimizes a risk measure [8] of its planned trajectory based on probabilistic forecasts of human motion from the data-driven predictor. A risk measure is a functional that maps a cost distribution to a deterministic real number, which lies between the expected cost and the worst-case cost [9].