用分层强化学习提升自动驾驶在复杂城市环境中的安全决策能力
Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments
- 分层POMDP框架建模车辆行为与轨迹的双重不确定性
- 通过驾驶风格推断提升对周围车辆意图的预判准确率
- 适合自动驾驶系统开发与智能交通研究者参考
动态道路环境中存在的不确定性给自动驾驶的行为与轨迹规划带来重大挑战。本文提出Hi-Drive，一种基于分层部分可观测马尔可夫决策过程（Hierarchical POMDP）的规划算法，在行为与轨迹两个层面同时处理不确定性。该方法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隐藏的驾驶风格。将驾驶员模型作为高层决策动作，有效缓解了传统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引入基于重要性采样的轨迹优化机制，通过对关键交通参与者进行全面分析来精细化轨迹。在真实城市驾驶数据集上的评估表明，Hi-Drive在多种城市驾驶场景下显著优于当前最先进的基于规划与基于学习的方法。
原文摘要 · Abstract (English)
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.
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