Uncertainties in dynamic road environments pose significant challenges for behavior and trajectory planning in autonomous driving. This paper introduces Hi-Drive, a hierarchical planning algorithm addressing uncertainties at both behavior and trajectory levels using a hierarchical Partially Observable Markov Decision Process (POMDP) formulation. Hi-Drive employs driver models to represent uncertain behavioral intentions of other vehicles and uses their parameters to infer hidden driving styles. By treating driver models as high-level decision-making actions, our approach effectively manages the exponential complexity inherent in POMDPs. To further enhance safety and robustness, Hi-Drive integrates a trajectory optimization based on importance sampling, refining trajectories using a comprehensive analysis of critical agents. Evaluations on real-world urban driving datasets demonstrate that Hi-Drive significantly outperforms state-of-the-art planning-based and learning-based methods across diverse urban driving situations in real-world benchmarks.

翻译：动态道路环境中的不确定性为自动驾驶的行为与轨迹规划带来了重大挑战。本文提出Hi-Drive算法，该算法采用分层部分可观测马尔可夫决策过程（POMDP）框架，在行为层与轨迹层同时处理不确定性。Hi-Drive利用驾驶员模型表征其他车辆的不确定行为意图，并通过模型参数推断其隐含的驾驶风格。通过将驾驶员模型作为高层决策动作，本方法有效缓解了POMDP固有的指数级复杂度问题。为进一步提升安全性与鲁棒性，Hi-Drive融合了基于重要性采样的轨迹优化方法，通过对关键交通参与者的综合分析实现轨迹精细化调整。在真实城市驾驶数据集上的评估表明，在多样化城市驾驶场景的实际基准测试中，Hi-Drive显著优于当前基于规划与基于学习的先进方法。