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The paper introduces quantile propagation planning (QPP), which propagates a fixed set of disturbance quantiles through dynamics to evaluate both mean and tail risk under a limited model-evaluation budget, showing reduced tail cost and constraint violations over mean-rollout planning on overload and inventory tasks.
Proposes a unified risk map modeling framework for autonomous driving that integrates traffic flow and collision risks in partially observable environments, using spatiotemporal modeling and diffusion-based scenario generation. Outperforms state-of-the-art occlusion-aware baselines on the Waymo Open Motion Dataset.