  Decision-making in dense traffic scenarios is challenging for automated
vehicles (AVs) due to potentially stochastic behaviors of other traffic
participants and perception uncertainties (e.g., tracking noise and prediction
errors, etc.). Although the partially observable Markov decision process
(POMDP) provides a systematic way to incorporate these uncertainties, it
quickly becomes computationally intractable when scaled to the real-world
large-size problem. In this paper, we present an efficient uncertainty-aware
decision-making (EUDM) framework, which generates long-term lateral and
longitudinal behaviors in complex driving environments in real-time. The
computation complexity is controlled to an appropriate level by two novel
techniques, namely, the domain-specific closed-loop policy tree (DCP-Tree)
structure and conditional focused branching (CFB) mechanism. The key idea is
utilizing domain-specific expert knowledge to guide the branching in both
action and intention space. The proposed framework is validated using both
onboard sensing data captured by a real vehicle and an interactive multi-agent
simulation platform. We also release the code of our framework to accommodate
benchmarking.
