誘導分岐を用いた自動運転のための効率的な不確実性考慮型意思決定
Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching
ドメイン知識で分岐を誘導するDCP-TreeとCFBにより、POMDPベースの意思決定を実時間で計算可能にした自動運転フレームワークを提案し、実車とシミュレーションで検証した。
著者: Lu Zhang, Wenchao Ding, Jing Chen, Shaojie Shen
分類: cs.RO
原文アブストラクト
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.
