  Autonomous driving systems face the formidable challenge of navigating
intricate and dynamic environments with uncertainty. This study presents a
unified prediction and planning framework that concurrently models short-term
aleatoric uncertainty (SAU), long-term aleatoric uncertainty (LAU), and
epistemic uncertainty (EU) to predict and establish a robust foundation for
planning in dynamic contexts. The framework uses Gaussian mixture models and
deep ensemble methods, to concurrently capture and assess SAU, LAU, and EU,
where traditional methods do not integrate these uncertainties simultaneously.
Additionally, uncertainty-aware planning is introduced, considering various
uncertainties. The study's contributions include comparisons of uncertainty
estimations, risk modeling, and planning methods in comparison to existing
approaches. The proposed methods were rigorously evaluated using the CommonRoad
benchmark and settings with limited perception. These experiments illuminated
the advantages and roles of different uncertainty factors in autonomous driving
processes. In addition, comparative assessments of various uncertainty modeling
strategies underscore the benefits of modeling multiple types of uncertainties,
thus enhancing planning accuracy and reliability. The proposed framework
facilitates the development of methods for UAP and surpasses existing
uncertainty-aware risk models, particularly when considering diverse traffic
scenarios. Project page: https://swb19.github.io/UAP/.
