Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments

• URL: http://arxiv.org/abs/2409.18411v2
• Date: Wed, 15 Oct 2025 07:52:38 GMT
• Title: Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments
• Authors: Xuanjin Jin, Chendong Zeng, Shengfa Zhu, Chunxiao Liu, Panpan Cai,
• Abstract summary: Hi-Drive is a hierarchical planning algorithm addressing uncertainties at both behavior and trajectory levels.<n>Hi-Drive employs driver models to represent uncertain behavioral intentions of other vehicles.
• Score: 4.76355503514683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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