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

TL;DR

Hi-Drive is a hierarchical POMDP-based planning algorithm that improves autonomous driving safety and robustness in complex urban environments by modeling driver behaviors and optimizing trajectories.

Contribution

The paper introduces Hi-Drive, a hierarchical POMDP framework that manages behavioral and trajectory uncertainties using driver models and importance sampling.

Findings

Outperforms state-of-the-art methods in urban driving scenarios

Effectively models driver intentions and styles

Enhances safety and robustness in real-world benchmarks

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.