Uncertainties in dynamic road environments pose significant challenges for behavior and trajectory planning in autonomous driving. This paper introduces BoT-Drive, a planning algorithm that addresses uncertainties at both behavior and trajectory levels within a Partially Observable Markov Decision Process (POMDP) framework. BoT-Drive employs driver models to characterize unknown behavioral intentions and utilizes their model parameters to infer hidden driving styles. By also treating driver models as decision-making actions for the autonomous vehicle, BoT-Drive effectively tackles the exponential complexity inherent in POMDPs. To enhance safety and robustness, the planner further applies importance sampling to refine the driving trajectory conditioned on the planned high-level behavior. Evaluation on real-world data shows that BoT-Drive consistently outperforms both existing planning methods and learning-based methods in regular and complex urban driving scenes, demonstrating significant improvements in driving safety and reliability.

翻译：动态道路环境中的不确定性给自动驾驶的行为与轨迹规划带来了重大挑战。本文提出BoT-Drive，一种在部分可观测马尔可夫决策过程（POMDP）框架下同时处理行为层与轨迹层不确定性的规划算法。BoT-Drive采用驾驶员模型来表征未知的行为意图，并利用其模型参数推断隐藏的驾驶风格。通过将驾驶员模型同时作为自动驾驶车辆的决策动作，BoT-Drive有效应对了POMDP固有的指数级复杂度。为提升安全性与鲁棒性，该规划器进一步应用重要性采样方法，在已规划的高层行为基础上优化行驶轨迹。基于真实场景数据的评估表明，在常规及复杂城市驾驶场景中，BoT-Drive在驾驶安全性与可靠性方面均显著优于现有规划方法及基于学习的方法。